Strategic insight and guidance to assist higher education institutions in leveraging the benefits of technology&nbsp; – from artificial intelligence to campus infrastructure

Digital transformation

In recent years, universities have been paying increased attention to sexual misconduct on campus. Many institutions have turned to mobile apps and online reporting platforms to provide a pathway for capturing incidents and supporting students and staff. While these tools hold great promise, our research highlights that many need better security and documentation features. Through our in-depth analysis of platforms that are in use around the globe, we offer six key recommendations for universities adopting these tools.&nbsp;1.&nbsp; Prioritise survivor safety and confidentiality&nbsp;Exposing sensitive information, such as personal details or the nature of the incident, can be devastating to the individuals involved, leading to further trauma and a sense of betrayal. We found in our research that many platforms have limited security features, potentially compromising users’ safety. Furthermore, some tools do not provide information about how to use the app nor state how the organisation will use their data or include an informed consent procedure. Institutions should vet potential partners and vendors thoroughly and require them to provide detailed information about their data security measures, including encryption methods, access controls and data retention policies. These practices should be evaluated against the most rigorous standards for protecting survivor privacy.&nbsp;Addressing sexual misconduct in higher education, part one: preventionGender equality is more than a box-ticking exercise – let’s champion itEight ways to improve responses to sexual misconduct in universitiesWhile anonymous reporting poses challenges for investigations and for supporting survivors in the aftermath of an incident, it is crucial for encouraging people to come forward. Research shows that loss of anonymity and confidentiality can deter people, especially those from marginalised communities, from reporting incidents. Survivors may fear negative social judgement or that the perpetrator will retaliate. Allowing users to come forward anonymously or confidentially empowers survivors to speak out.2.&nbsp; Ensure equitable access and use trauma-informed language&nbsp;Technology that is hard to use or that is inaccessible to some users is a barrier to reporting. Universities should look for tools that incorporate features such as text-to-speech, adjustable text sizes and high-contrast viewing modes, which were rare in the tools we evaluated. It is also important that the platform uses plain language that’s sensitive to the impacts of trauma and avoids the use of legal or medical jargon that can come across as alienating or victim blaming. Every aspect of the platform and process should convey a genuine commitment to inclusivity and compassion.3.&nbsp; Establish diverse advisory groups to support accountability and risk mitigation&nbsp;In our work with survivors’ organisations, we’ve seen the value of bringing in diverse stakeholder perspectives. Universities should establish advisory groups made up of students, academic staff, and survivors and their advocates, as well as other topic experts. These stakeholders can provide valuable guidance on platform design, help spot potential issues early on and make sure the system is responsive to changing user needs. Regularly seeking input from people with different experiences is key to anticipating unintended consequences and figuring out how to avoid them. Advisory boards also play a vital role in promoting transparency and building trust with the campus community.4. Include contingency planning for data preservation and ongoing user support&nbsp;The tech start-up world can be unpredictable, and systems developed via researchers and non-profits can disappear when the funding runs out. Institutions should require comprehensive plans from vendors for protecting and transferring sensitive data if the company goes under. Working with university legal advisers and law enforcement is essential in thinking through potential issues around data discoverability in legal contexts. Tackling these scenarios head-on is a must for living up to promises of survivor care and making sure crucial evidence isn’t lost forever.5. Capture survivor statements verbatim&nbsp;Open-ended questions let people share authentically, in their own words. This protects against the risk of an account being distorted by another party. Systems must also prevent survivors seeing each other’s reports. Reading similar accounts might seem comforting, but it can seriously undermine survivor credibility. We know from our research that any hint of blurred lines can suggest to legal officials and juries that the survivor’s memory of the incident has been contaminated by other sources. Universities must safeguard the integrity of each person’s individual truth. Every voice deserves to be heard, untainted, so the focus stays on accountability and justice.6. Invest time and energy in understanding people’s concerns and experiences&nbsp;It is essential to conduct regular surveys on perceptions of campus safety and barriers to reporting. Collecting this data before and after rolling out a new reporting system creates a baseline for measuring impact and figuring out how to make it work better for everyone. University leadership should also seek out evaluations and listen to survivors and advocates, making sure the platform is meeting people’s needs. Building that continuous dialogue establishes trust and accountability when it comes to preventing and responding to sexual violence.We desperately need innovative solutions to address the problem of sexual violence at universities. Technology offers exciting possibilities for empowering survivors and shedding light on systemic failures. But universities can’t just throw an app at the problem and hope for the best. Designing and implementing reporting platforms requires careful consideration of the ethical and evidentiary challenges involved. It’s a weighty responsibility with high stakes for survivors and the integrity of the academic community.Our research underscores just how critical it is for universities to approach this task with the utmost care and commitment. By prioritising survivor safety, ensuring equitable access, bringing in robust oversight, planning for all possible scenarios, committing to continuous improvement and centring the needs of diverse students and staff, institutions can tap into the potential of technology to drive meaningful change. This demands responsible stewardship, a fierce dedication to survivor-centred values and a willingness to keep listening, learning and adapting. With the right intentions and tools, sexual misconduct reporting tools on campus can be a powerful ally in the pursuit of protection, healing and justice.Heather D. Flowe&nbsp;is professor of psychology&nbsp;and&nbsp;Laura M. Stevens is a final-year PhD student, both at the&nbsp;University of Birmingham.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

How to choose an app for reporting sexual violence on campus

Responsible technology can enhance university responses to gender-based violence reports. Here, Heather Flowe and Laura Stevens share what to look for and demand when choosing an app or platform

How to choose an app that will encourage reporting of sexual violence on campus

Artificial intelligence has infiltrated several aspects of our lives, and we are slowly acclimatising to the idea of its being constantly around us; AI nudges us to make decisions, and many of us can relate to the feeling of its ghost-like presence. We must accept that it is here to stay and is likely to become even more present in our daily lives in the months and years to come.AI is reshaping education in many ways. To what extent will it disrupt it? As professors, are we prepared to handle this disruption? Should we take it on or partner with it? These are all valid questions that require some thought.AI in assessmentsWe can use AI to create assessments, assign difficulty levels and automate grading. Coursera harnesses the power of AI to generate content and assessments tailored to diverse learning objectives. Through AI-driven algorithms, it creates a wide array of educational materials, including assessments that align closely with course objectives.The process begins with AI algorithms curating content in varying forms from a vast pool of resources, ranging from textbooks and online materials to academic papers and lecture notes. AI can then generate assessment items, including multiple-choice questions, true/false statements, questions requiring short answers or longer, more complex ones. These assessments are designed to evaluate students’ understanding of key concepts, problem-solving abilities and critical thinking skills.However, it’s important to note that while AI aids in the creation of assessments, the final quality check rests with the professor. Before sharing assessments with students, professors must review and validate the questions to ensure their appropriateness and difficulty level.This collaborative approach between AI technology and human expertise ensures that assessments meet the required standards and effectively evaluate students’ understanding of course content.Resource collection: AI transformers like ChatGPT are here, so what next?AI can help fix student evaluationsHow can we teach AI literacy skills?Customised learning experiencesAI can help us identify students’ understanding levels and create tailor-made learning experiences. We can use it to track students’ performance and generate better recommendations and questions. We can use it to analyse students’ historical data, identify trends and suggest strategies to help them improve.Real-time feedbackAI-driven performance analytics provide students with real-time insights into their academic progress. By aggregating data from various assessments and learning activities, AI systems can generate comprehensive performance reports, highlighting areas of strength and where improvement is needed. This immediate feedback empowers students to take proactive measures to enhance their learning outcomes.AutomationAI algorithms can automatically grade multiple-choice and objective questions. They analyse the provided answers against the correct solutions, swiftly determining correctness without human intervention. This process drastically reduces grading time. We can also consider using natural language processing techniques to assess short answers or essays to save time. By automating routine grading tasks, we can spend more time on activities such as providing personalised feedback, designing instructional materials and engaging with students. Increased efficiency enhances educators’ productivity and enables them to devote more attention to enhancing teaching and learning experiences.Adaptive course designFaculty members can leverage AI to craft interdisciplinary courses and courses tailored to meet the unique needs of their students. To&nbsp;begin, educators must identify the intersecting areas from different disciplines and establish clear learning objectives. These objectives serve as guiding principles for course development. We can then use AI to generate content aligned with these objectives. This content may consist of curated materials such as articles, videos and other resources sourced from various disciplines.AI’s capability to offer multimodal feedback encompassing audio, video and visuals opens up opportunities for creating engaging learning content. By combining text-based information with audio recordings and visuals, educators can enhance student engagement significantly. This approach facilitates a dynamic learning experience that caters to diverse learning preferences and fosters a deeper understanding of interdisciplinary concepts.Innovative teaching methodsAI enables educators to experience innovative teaching methods such as gamification and simulations. Gamification elements such as points, badges and leader boards motivate students to engage more with the content. As with video games, students can use their points to unlock the next level of learning thus encouraging further understanding and mastery. Such interactive learning experiences enhance student motivation and understanding, leading to better learning outcomes. Simulation games immerse students in real-life scenarios and encourage them to make strategic decisions against virtual competitors. These kinds of simulations are useful for teaching business strategies.Thanks to its adaptability, we can introduce AI into our teaching in various disciplines. We can teach topics such as sustainability by exploring the role of AI in energy optimisation, waste reduction and others. Similarly, we can weave the use of AI into subjects such as the internet of things and design, enriching and modernising the curriculum. As educators, we must focus on finding interesting ways to do this in our chosen areas.AI is revolutionising higher education by helping educators offer more personalised learning experiences, improving assessment practices and improving overall educational outcomes. It can significantly enhance faculty productivity, teaching and student learning, so let us treat it as a friend and leverage its power to help higher education evolve and prepare students for living in a digital age.K.V. Sriram is an a professor in the department of humanities and management, Manipal Institute of Technology, India.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

How to partner with AI and improve the learning experience

As generative AI permeates the classroom, educators are faced with a very important question: is it a friend or a foe? 

As educators, how can we craft questions that effectively encourage critical thinking? How can we write the right questions? And how can we use artificial intelligence to link concepts and foster critical thinking among our students?&nbsp;At the core of critical thinking lies the skill of posing deep and thought-provoking questions. In the changing landscape of online learning, redesigning the ways in which we approach reasoning, analysis and synthesis is paramount. Critical thinking skills are particularly important in the online courses as they enable students to analyse information critically, evaluate sources effectively and make informed decisions independently in a digital learning environment.&nbsp;AI and assessment redesign: a four-step processHow can we teach AI literacy skills?Embracing generative AI as a co-design partner for designing and developing coursesAI can create engaging and effective learning experiences for educators and students. On one side, educators can use generative AI to tailor instruction, automate tasks and provide personalised feedback, freeing up valuable time. On the other side, students can use AI to connect, explore diverse perspectives and visualise complex data.But how can generative AI help educators and students focus on the higher-order skills needed to evaluate and reason though complexities? Here, I’ve identified five ways to do so.Using AI to connect ideasCritical thinking entails assessing, integrating and examining information to make well-informed choices, guided by criteria and connections.&nbsp;AI technology helps to connect ideas, merging them to create stronger concepts and produce a wide range of ideas quickly.&nbsp;For example, in a genetics course, providing a paper with a critical thinking question on researching genetic engineering across different fields. Use this example prompt to generate an assignment for students to answer and make connections:&nbsp;"Genetic engineering techniques have advanced rapidly in recent years, allowing scientists to precisely edit genes and modify the genetic make-up of organisms. With these powerful tools, we can now alter the genetic code of organisms in once unimaginable ways. Your task is to use an AI system to explore the potential applications and implications of genetic engineering in various fields, such as agriculture and food production, medicine and human health, creation of a new species and environmental conservation.”&nbsp;Throughout the process, for example, the students could document the initial ideas and the connections made between concepts, or observe the similarities and differences produced. Then they could reflect with their own analysis by comparing their thoughts on each area. Students could use Chat PDF to interact with research articles and analyse the information within the article, using AI critically.&nbsp;Effective critical thinking involves constructing well-reasoned arguments supported by evidence. Towards the end of the course, students should be able to critically evaluate emerging technologies and their impact on society and the environment.Synthesis, visualisation and practiceEducators can empower students with instructional methods that integrate synthesis and visualisation of assignments. Generative AI can assist students in analysing and visualising complex scientific data, helping them identify patterns, trends and anomalies, which are crucial for developing critical thinking skills in the sciences. For example, Google’s Gemini can generate images with text, using prompts such as “visualise the virus particle that causes rubella”.Create AI-powered presentationsThinking critically is an ongoing process of questioning, analysing and refining one’s understanding. Gemini’s ability to quickly generate and modify slides, based on new information or feedback, can facilitate this iterative process. It can now create PowerPoint slides complete with speaker notes for educators and students alike.&nbsp;For instance, the speaker notes feature can prompt students to articulate the logic and evidence behind each slide, fostering their ability to build and communicate sound arguments.&nbsp;As students explain their reasoning in the speaker notes, they can practise organising their thoughts, anticipating counter-arguments and strengthening their justifications, all of which are essential critical thinking skills.&nbsp;Educators can require students to use Gemini to explore alternative perspectives, incorporate additional evidence or restructure their presentations with AI. The iterative cycle can reinforce the habit of continuously re-evaluating and improving one’s thinking, essential for developing robust critical thinking skills. Other AI tools that can help make presentations include Gamma AI, Canva, Visme AI presentation maker and Tome. Not all the programmes are free.Develop virtual learningCritical thinking often involves visualising abstract concepts, relationships and processes. For instance, freely available online animations or YouTube videos can provide visual simulations of enzymes interacting with substrate. These visuals can help educators teach the importance of rates of reaction and enzyme substrate specificity. Animations such as these, along with generative AI critical thinking questions, can make customised virtual critical thinking assignments, which can be modified to different course levels.&nbsp;Synthesis and researchGenerative AI could be used to do some of the initial research on a given topic. Researchers could use a prompt such as: “I am in search of some cutting-edge research papers related to [insert topic] that have been published in the last five years. It would be great if you could direct me to some innovative and informative papers that could help me gain a deeper understanding of the topic.”Generative AI can be a powerful tool for supporting critical thinking in online courses. Educators can move beyond rote memorisation and ignite the critical thinking skills students will need to thrive in our constantly evolving world. Students can leverage AI to challenge assumptions, connect disparate pieces of information and reinforce foundational concepts. We have the opportunity to harness the powers of new technology to equip the next generation with the skills to assess their world critically – let’s not waste it.Urbi Ghosh is a science faculty member in General Education at CSU Global.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

Use AI to get your students thinking critically

When crafting online courses, teaching critical thinking skills is crucial. Urbi Ghosh shows how generative AI can shape how educators can approach this

Use artificial intelligence to get your students thinking critically

Just 18 months after the launch of ChatGPT, our world is now shot through with the use, results and implications of generative artificial intelligence (AI). On campus, all areas of teaching, learning and student life have felt its impact, though probably none quite so much as assessment.&nbsp;It might be tempting to simply ban generative AI use in courses to avoid having to make changes to assessments (and to avoid complicity in the use of dubiously ethical technological tools), but this approach would miss out on opportunities to prepare and empower students, as well as to create more effective ways of assessing student learning. Though one may, after consideration, ban its use (there are good reasons to do so, pedagogical and otherwise), the fact remains that generative AI has quickly permeated everyday life and most professional work contexts. Students and instructors must spend time carefully charting ethical pedagogical paths through this new terrain. A considered interplay between designing for thoughtful deterrence and responsible promotion, characterised by non-punitive and open co-exploration are the two sides of the design coin for effective and empowering AI-era assessments.&nbsp;The four broad strategies outlined below can help faculty chart a path forward. Each strategy in use will amplify the effectiveness of the others. Implementing an idea or two from each of the broad strategies will put you and your students in a strong position to continue to meet the changes and challenges generative AI brings.Resource collection: AI transformers like ChatGPT are here, so what next?AI can help fix student evaluationsEmbrace AI tools to improve student writingEmploy relational and transparent pedagogiesWhen education feels like a transaction, students will respond in kind by making transactionally motivated decisions, like trying to use AI to reduce time spent on required assessments, especially if they cannot understand the rationale behind the assessment in the first place. In whatever ways suit your pedagogical style, employ strategies that increase relationship-building and pedagogical transparency whenever you see an opportunity to do so. This might look like:Auditing your assignments against what you know about these tools, discerning which aspects of assignments would retain their integrity even with AI use. Are you more concerned about an essay with a specific structure and linguistic clarity? Perhaps using AI, then, for brainstorming would not detrimentally affect student achievement of this outcome. Share this audit with your students – a demonstration of careful pedagogical consideration shows them that you are not arbitrarily prohibiting a tool that they perceive as useful or important.&nbsp;Seeking and garnering their thoughts and concerns, and sharing yours, in an early-term class session, showing that you want and appreciate their critical judgement. Incorporate some of these in course policies or expectations.Get creative with assessment structure, environments and loadThe prospect of four or five big, unwieldy essays or projects due for each course all in the same month will have students seeking shortcuts. Attend empathetically to the myriad challenges of contemporary students by altering the structure of assignments, where those assignments might be done and questioning the impulse to do “what we’ve always done.” Consider these ideas:Break down assignments by&nbsp;scaffolding or&nbsp;multi-staging, including detailed, step-by-step instructions. Removing the stress of managing an entire complex assignment can help relieve impulses toward procrastination and seeking AI shortcuts.&nbsp;Assignment end-products, or parts of the assignment process, might be better matched with a medium other than writing. A one-paragraph research topic assignment could be verbally delivered during office hours, or via audio clip on the learning management system.Create collaborative and communal assessment environments, bringing what is usually done alone and outside the classroom into the classroom. Consider approaches such as in-class&nbsp;writing circles that aim to help students overcome typical obstacles to writing (this example takes only 15 minutes) and&nbsp;collaborative final exams, such as the “Tentarium”, which combines the written exam and the seminar formats: students write the exam, then discuss with peers and tutorial leaders, then revisit the written exam to revise (more information below).&nbsp;Emphasise process over productMost instructors probably include verbs such as “interpret”, “distinguish”, “evaluate” and “investigate” in their assignment learning outcomes. The road to those intellectual actions is rocky and winding; students need to stumble around, experiment, engage in false starts, revise, start over, collaborate and discuss. So why do we so often solely require – and solely reward – a single, polished artefact (such as an essay) and, in doing so, prioritise product over process? Here are some ways to do the reverse:Help students make their work visible (and give them points for it) by requiring them to submit thinking and learning artefacts. For example, ask for a “tracked changes” draft in which the student shows how they incorporated or considered, instructor/peer feedback. Ask for&nbsp;meditations on their own thinking processes and work processes. These could be metacognitive reflections on how they approached the assignment or personal reflections.Use labour-based grading schemes. In other words,&nbsp;grade for the quality of effort, not for the quality&nbsp;of the product.Incorporate a critical AI lensStudents need the time and support to critically examine and explore new technological tools and imagine the implications (positive and negative) for their lives and the lives of those who belong in their communities. They need time to develop their ethical stances. Here are some ideas:Co-create course components such as syllabus language, assignment instructions, rubrics or course etiquette together. Tease out the specific implications of AI use.&nbsp;Set assignment prompts that help students explore the implications of AI use for their disciplines, their lives or their learning. Include readings in your course that discuss the pros and cons that students can draw upon in their explorations.Test out the AI together. Compare human and AI output (a piece of creative writing, a laboratory experiment) to understand the limits (be aware of any institutional policies surrounding the use of third-party software and data security and privacy).&nbsp;Faculty do not have to have AI (and their ethical and pedagogical stances to it) completely figured out before redesigning their assessments. In fact, building the “figuring it out” into assessments is a big part of making them effective as preparatory and empowering, building up students’ AI literacy and critical engagement.&nbsp;Kate Crane is an educational developer (online pedagogies) at Dalhousie University.

Designing assessments with generative AI in mind

A new era of AI requires a balance between thoughtfully mitigating and responsibly promoting student use of new tools

The integration of artificial intelligence (AI) into higher education is transforming how students learn and educators teach. From giving personalised learning experiences to instant feedback, AI is opening new doors for enhanced educational experiences​​.&nbsp;Recent studies underscore the benefits of AI in education. A systematic review from 2016 to 2022 of 138 articles revealed significant advancements in AI applications in higher education. These applications included assessment, prediction, assistance, intelligent tutoring and managing student learning, emphasising AI’s transformative role​​. Scholars have reported on AI’s ability to adapt instruction to learner types, provide customised feedback, develop assessments and predict academic success​​.However, this integration brings with it the concerns of de-skilling. As Gabi Reinmann’s&nbsp;2023&nbsp;discussion paper (in German) highlights, the term “de-skilling” refers to the potential erosion of human competencies, a subject that, while not extensively explored in the context of AI at universities, is of growing importance. As in sectors such as medicine and finance, where reliance on AI and automation has led to reduced hands-on skills and domain knowledge​​, there’s a risk in higher education, too. The extensive use of AI could diminish critical thinking, creative problem-solving and other essential skills.&nbsp;Four steps for integrating generative AI in learning and teachingTeaching AI literacy: how to beginUniversities, AI and the common goodFurthermore, ethical considerations, such as potential biases and privacy concerns in AI systems, add another layer of complexity to this rapidly evolving educational landscape​​. In light of these ethical challenges, how do we ensure that AI tools are used responsibly and inclusively in educational settings?&nbsp;Introducing the ‘deep learn’ frameworkTo navigate this AI-infused academic environment, I propose the “deep learn” framework. The acronym stands for: discover, engage, evaluate, probe, link, expand, adapt, reflect, navigate. It’s more than a set of guidelines; it’s a mindset for engaging with AI in a thoughtful, critical and ethical manner. Each element has a learning objective and practical exercises for students.&nbsp;Discover: encourage explorationObjective: Students use AI to uncover areas of study, research and interest.Application: For example, students might use AI to explore emerging trends in their fields, gather diverse perspectives or uncover historical data relevant to their studies.Engage: promote meaningful interactionObjective: Move beyond passive queries and actively engage with AI as a learning partner.Application: Students could use AI to simulate real-world scenarios or participate in AI-driven discussions on complex topics.Evaluate: foster critical assessmentObjective: Students can critically assess AI-generated information for accuracy, bias and relevance.Application: Students could compare AI responses with established research to identify discrepancies or confirmations, enhancing their critical-thinking skills.Probe: encourage in-depth enquiryObjective: Students understand the need to ask follow-up questions and delve more deeply into subjects to gain a comprehensive understanding.Application: Students can use AI to explore facets of a topic, asking iterative questions based on initial AI responses to gain deeper insights.Link: connect AI insights to academic conceptsObjective: Highlight the importance of integrating AI insights with the academic.&nbsp;Application: Students can draw parallels between AI-generated information and theoretical concepts from their coursework, fostering a holistic understanding and gaining insight into practical implications.Expand: broaden intellectual horizonsObjective: Students can use AI to explore perspectives and ideas beyond their current understanding.Application: Students might use AI to generate creative solutions for a project or to understand global perspectives on local issues.&nbsp;For example, students could use AI to analyse discussion forums and social media from different cultures to enhance a multicultural curriculum.Adapt: cultivate flexibilityObjective: Students develop adaptability in approach and thought, and the ability to adjust to the dynamic nature of AI.Application: Students learn to refine their queries based on AI responses, customising their learning approaches as needed.Reflect: instil thoughtful considerationObjective: Students can reflect on the broader implications, ethics and impacts of AI-generated information.Application: After interacting with AI (for example, to analyse case studies in the use of AI for hiring), students could reflect on how AI’s insights might affect societal issues, ethical considerations or future trends.Navigate: master AI’s capabilities and limitationsObjective: Students can work within AI’s strengths and weaknesses, using it to its fullest potential.Application: Students should learn to identify when AI is most useful and when human judgement is paramount, balancing AI’s capabilities with its limitations.&nbsp;The framework sets a foundation for students to interact with AI in a multifaceted and conscientious manner, addressing both the immense potential and the inherent challenges of AI in education. We must also envision a path that not only embraces these principles but also actively shapes the role of AI in enhancing educational outcomes and maintaining essential human skills.The way forward for AI in higher educationAcknowledging these challenges paves the way for a balanced approach to AI’s role in higher education. The focus should shift towards a model that harnesses AI’s potential to enrich learning experiences, while also actively countering the risks of de-skilling. This necessitates using AI in a way that complements and enhances human abilities (rather than supplants them). We need a collaborative relationship between human and machine intelligence​​.&nbsp;In practical terms, this translates into educational settings where AI is leveraged for personalised learning journeys, simultaneously fostering active, critical engagement from learners. Educators and institutions must prioritise skills that AI cannot replicate, such as empathy, ethical judgement and nuanced problem-solving. Policymakers and educational leaders are called upon to develop frameworks and policies that support ethical AI integration, promote digital literacy and safeguard against the erosion of essential human skills.&nbsp;Additionally, ongoing research should aim to understand the long-term impacts of AI in education, exploring effective hybrid teaching models and developing AI tools that are transparent, equitable and culturally sensitive. By steering AI integration with a thoughtful, human-centric perspective, higher education can leverage AI’s strengths while preserving and nurturing the skills that are quintessential to human intellect. This balanced strategy ensures that AI becomes a powerful ally in the realm of education, fortifying rather than replacing the critical human skills and competencies at its core.Birgit Phillips is director of academic professional development and AI at&nbsp;FH Joanneum&nbsp;University of Applied Sciences in Graz, Austria, and founder of&nbsp;Phillips Learning Lab.&nbsp;If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

‘Deep learn’: a framework for AI literacy

AI literacy is no longer a futuristic concept; it’s a critical skill for university students. The ‘deep learn’ framework offers a comprehensive approach to enhancing literacy around artificial intelligence and application in higher education settings

The ‘deep learn’ framework: elevating AI literacy in higher education

Risks to the academic integrity of HE assessments have increased significantly with the wider availability and sophistication of generative artificial intelligence (GenAI) tools. Addressing these requires a genuine institutional commitment to staff development, support and time for transformative work.If GenAI tools have ushered in an era in which institutions can no longer assure the integrity of each individual assessment, the sector must focus on assuring the integrity of awards. There is a growing consensus that to do this, assessments should demonstrate a systemic approach to programme learning outcomes by securing academic integrity at meaningful points across a programme of study rather than at individual course/unit level. But how can we do this sustainably, given the existing pressures on academic and professional staff? ­Review and categoriseStep one requires academics to complete a self-review of each assessment task, ie, how likely it is that AI can complete a task. Based on their knowledge and subject matter expertise, staff must consider several risk factors to establish a low/medium/high rating:Risk factorDescriptionTypeSome task types (for example, a written essay) will have a higher level of risk than others (eg, oral presentation or invigilated exam).ContextTasks based on very specific material, or that involve authentic application to novel real-world situations, are likely to have lower risk than tasks based on more generally available information or basic concepts.ConditionsFully online assessments may involve higher levels of risk than those that involve some in-person component.OutputTasks that involve the creation of an artefact (for example an essay or report) may involve higher levels of risk than tasks that involve a performance component.SubmissionTasks that end at submission may have a higher level of risk than those that involve a post-submission discussion or follow-up (for example, a Q&amp;A post presentation).Quality of AI&nbsp;outputHigh-quality AI output is a risk to academic integrity because it may not be recognised as AI output. Staff should test their assessment via a secure GenAI platform and consider the output quality.InvigilationFully invigilated tasks have a comparatively low risk.Map and analyseStep two involves producing maps detailing the use of assessment tasks (with risk ratings) across programmes. Discipline groups carefully consider and identify tasks that are key to students’ achievement of programme-level learning outcomes. They must make reasoned decisions about whether the potential use of AI compromises the academic integrity of these key programme assessments, or whether there are gaps that need to be filled by potential assessment redesign.They can then prioritise the critical programme assessments that are identified as at risk for assessment reform initiatives. Prioritisation should include triangulation with other factors such as the number of students enrolled in the programme and its overall strategic importance. Any assessment tasks identified as being of lesser importance to the achievement of programme outcomes may offer opportunities to incorporate the use of AI in learning and assessment to help students engage responsibly and ethically with AI.AI resources for higher education professionals&nbsp;How can we teach AI literacy skills?We need to address the generative AI literacy gap in higher educationReform assessmentStep three involves the planning of assessment reform initiatives that secure the resources (human and technical) required to implement meaningful assessment reform on a potentially large scale. Learning designers who can help identify and implement appropriate measures to ensure the integrity of key programme assessments must support academic staff at this stage. Approaches will be sensitive to disciplinary norms but are likely to involve a mix of moving assessments to secure testing platforms, for example, online proctoring adding additional components to existing tasks such as a presentation or debate or introducing entirely new task types, such as interactive oral assessments.GovernanceFinally, in step four, given the effort required to undertake large-scale assessment reform, teaching and learning committees should review associated governance mechanisms to ensure an ongoing quality cycle.Samuel Doherty is the education and innovation coordinator, and Steven Warburton is pro vice-chancellor for education and innovation at the University of Newcastle, Australia.

AI and assessment redesign: a four-step process

If GenAI tools have ushered in an era in which institutions can no longer assure the integrity of each individual assessment, the sector must focus on assuring the integrity of awards, write Samuel Doherty and Steven Warburton

As the digitalisation of industry grows, higher education institutions must equip their students with the skills to thrive in the workplace of tomorrow. But this is not an easy task, considering the range of digital competencies that might be required. Rapid change in the technology landscape, which could make course materials outdated before the students even graduate, makes the challenge even more difficult.Three approaches to improve your online teachingThe three stages of developing a framework to support students of open and distance learningDigital marketing for universities: strategies for successAt the Singapore Institute of Technology (SIT), we recently embarked on a journey to enhance the digital competencies of our students and academic staff. Here is what we learned.Designing a framework for developing digital competencies of the studentsIn Singapore, one of the initiatives under the Ministry of Education’s (MoE) EdTech Masterplan is to strengthen the development of students’ digital literacy and technological skills. MoE also sets forth the digital competency baseline requirements that must be achieved by all graduates from the local higher education institutions.&nbsp;To fulfil these requirements for our students at SIT, a Digital Competency Framework was developed and implemented as shown in the following diagram:
  
      
  
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&nbsp;This framework includes a Digital Competencies Essentials (DiCE) University-Level Module (ULM) that will have to be completed by all students in the university regardless of their discipline. A Digital Skills and IT Enrichment (dSiTe) framework has also been developed, separating digital skills into two categories, generic or specialised; 27 digital competencies, e.g. robotics, mobile computing, blockchain, AR/VR; and five levels of attainment –&nbsp;as follows:
  
      
  
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All degree programmes must also include one or more projects for the students to develop and demonstrate some of these specialised technical digital skills. Here is our approach and some of the considerations that were taken in during the implementation of the framework:The importance of asynchronous online micro-modules: Students across different degree programmes are going to have different study loads and timetables. So, instead of having scheduled face-to-face classes, DiCE is divided into multiple asynchronous online micro-modules, each covering specific digital competency areas as shown in the diagram below. This allows students the flexibility to develop their digital competencies anywhere and anytime across a stipulated period (in our case, the first six trimesters of their degree programmes). Students can personalise their learning plans and develop their digital competencies at their own comfortable pace.
  
      
  
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&nbsp;Supplement online contents with optional face-to-face support:&nbsp;We supplemented micro-modules with face-to-face practical workshops and consultation sessions. But we made them optional, because we felt students with backgrounds in IT or some knowledge already shouldn’t be forced to sit through a workshop they didn’t need.&nbsp;Targeting the students who felt they needed help meant we could optimise the manpower resources required to support the implementation of the framework.Leverage on external resources when possible: You don’t necessarily need to create all the materials for developing digital competencies from scratch, in-house.&nbsp;Many of the major tech companies have established academies with free courseware. These academies also provide free resources and platforms for students to develop their technical skills on (e.g. AWS Educate for cloud skills).&nbsp;Develop a systematic improvement cycle to ensure regular updates to the materials:&nbsp;Digital technology advances rapidly, so make sure to update the materials regularly. But also be aware of minimising the disruption to the students who are currently completing existing micro-modules. Take into consideration the following:Black out dates to allow for updates to the micro-modules.Separate course sites on the Learning Management System (LMS) for each cohort so that major changes to the requirements, such as the insertion or removal of compulsory micro-modules, can be done.Establish an Industry Advisory Committee (IAC) as well as a panel of Subject Matter Experts (SMEs) to provide regular validation of the digital competency frameworks and the micro-modules.Ensure the digital competencies of the academic staff:&nbsp;Another benefit of developing online asynchronous micro-modules is that these materials could easily be extended to develop the digital competencies of academic staff. For example, to ensure that all SIT academic staff with teaching duties have a minimum baseline digital competency for teaching and learning, they will have to compete the DiCE micro-modules on Adopting a Digital Mindset, Data Analytics, as well as AI within a year of joining the university. They will also have to submit an assignment on how generative AI tools are applied in modules they teach, then submit a reflection on the usage, implementation method, impact and ethical issues, among other topics.The current pre- and post-assessment results from the development of the digital competency framework are encouraging, with students demonstrating a 23 per cent improvement across all micro-modules taken. The effectiveness of online micro-modules to allow students to learn asynchronously and at their own pace is clear. We will continue to enhance the framework through the systematic improvement cycle, featured above, and will also create more opportunities for students to pick up digital skills at their own pace in the future.Steven Wong is Director of the Centre for Digital Enablement at the Singapore Institute of Technology.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus&nbsp;newsletter.

Develop a digital competency framework everyone can use

It’s up to higher education institutions to teach students the digital skills they’ll need for the world of work. Steven Wong outlines the framework his team developed to educate students and staff

How to develop a digital competency framework everyone can benefit from

THE podcast: Mark Thompson, professor of digital economy

Academic, practitioner and policy commentator Mark Thompson shares his concern that UK higher education is drifting from its true north of research, teaching and impact in the wake of complex digital change and the prisoner’s dilemma of whole-sector transformation

THE podcast: interview with Mark Thompson, professor of digital economy at the University of Exeter

Content creator and business coach Roberto Blake recently articulated the problem educators face in the brave new world of generative AI on X (formerly Twitter), posting: “The Terminator won’t kill you on the battlefield of 2045... The Terminator will kill you in the JOB MARKET of 2025... and there is no John Connor...” &nbsp;What will university graduates do, if ChatGPT is doing everything already?&nbsp;Is it true that large language models like ChatGPT can do everything, or is this an exaggeration? Tell that to the call centre representatives of Klarna, the payments facilitator. The global company announced in February 2024 that its AI assistant powered by OpenAI (the creators of ChatGPT) has had 2.3 million conversations, two-thirds of Klarna’s customer service chats, and is doing the equivalent work of 700 full-time agents. Not employing these agents is estimated to drive a $40 million (£32.1 million) profit improvement to Klarna in 2024. Great news for shareholders, not-such-great news for call centre operators.How can we teach AI literacy skills?Transition from expert to guide by embracing generative AIHow to strengthen your metacognitive skills to collaborate effectively with AISurely, this can’t affect highly qualified workers with university degrees, can it? If you are in the business of producing law graduates, you should be worried. The law firm A&amp;O launched a contract drafting tool, ContractMatrix, created in partnership with Microsoft and the AI lawyer Harvey, which is being sold as “software as a service”. ContractMatrix uses AI and other techniques to perform the grunt work of contract negotiation and drafting, leaving lawyers free to do more (highly billable) strategic thinking. A&amp;O claims that the tool can save around seven hours when negotiating a contract. These are seven hours that a paralegal or trainee would have spent &nbsp;–&nbsp;therefore, less need for juniors. Similar things are happening across the service industries.As an educator, you have a huge problem in 2024. Your graduates are about to be beaten in the job market by the AI in 2025. They won’t even make it to interview. What, if anything, can you do about it?&nbsp;In my podcast, I argue that you have two choices when your job is threatened by technology. You can be cheaper or better than the machine. Given that AI functionality is now integrated into tools we use for free or have paid for already, the first option is difficult – if not impossible.How, then, can educators help students be better than the machine? I propose two ways of doing this.Embrace the futureThe first way to beat AI is to embrace it. Higher education’s initial reaction to generative AI has been fear, followed by panic. Lecturers discovered that large language models could write fluently and worried that ChatGPT would be writing everyone’s assessments. Once they realised that there’s no way to stop this, panic ensued. Many institutions reacted with a return to campus-based invigilated assessments.&nbsp;This is a little like reacting to copy-pasting by advocating a return to marble plaques and hammers. The world has moved on whether we, or our external examiners, want it to or not. There is no point shielding our students and our courses from technology, only for these same students to emerge into a workplace dominated by this technology. No one wants a master’s degree in shovelling when all jobs require a mechanical digger operator. Embracing change means teaching our students how best to use AI tools to accelerate and improve their work –&nbsp;and yes, this includes writing essays.Teach human skillsThe second way to beat AI is through humanity. Universities have long argued that their focus is skills, rather than knowledge transmission in the abstract. We strive to give our students both knowledge and the tools to use this knowledge in a professional context (and beyond it). The skill that will be most in demand in a workplace dominated by AI will be human contact and personality. Humans like talking to other humans and, while most of us are happy to buy car insurance when the quote is generated by a bot, we would prefer to discuss how to turn around our business with an entrepreneurship coach. AI offers capacity, competence and speed, but not personality, warmth and empathy –&nbsp;or at least, not right now.&nbsp;Some graduates will outrun the Terminator. John Connor lived long enough to find a time machine to send someone to 1984. The graduate who survives is the one who knows how to use the machine and can do what it can’t: be human. Let us strive, as educators, to arm our students with humanity, before we launch them into the workplace of 2025.Ioannis Glinavos is a senior lecturer in law at the University of Westminster.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

How your graduates can beat AI in the job market of tomorrow

Your students are facing a competition against a faster, better and cheaper opponent in 2025’s job market – AI. Ioannis Glinavos offers his advice on prepping them for the fight

It is an exciting time to be in higher education. The development of generative AI has prompted academia to rethink teaching and learning. This pivotal moment we find ourselves at, as technology advances rapidly, offers an opportunity for educational reform. To do so, we must begin by addressing three fundamental questions.&nbsp;Question 1: What is learning?Educators are understandably concerned that students now think of learning as answers they get from GenAI, which they may not understand or even read. They may also think a summary from GenAI is the most efficient way to acquire knowledge as opposed to reading textbooks and course materials. This is compounded by the fact that these GenAI results may not be accurate or reliable. &nbsp;With students relying on GenAI in this way, we must revisit the question of what learning truly is. Higher education institutions worldwide have responded to this, revising their courses in various ways.How AI can revolutionise the way we analyse student surveysTransition from expert to guide by embracing generative AIChatGPT and the future of university assessmentBut these reviews need to go beyond incorporating GenAI into lessons and instead look at how this new technology changes approaches to pedagogy, as well as to course and programme structures. We can design micro and macro learning pathways to elevate GenAI, encouraging adaptive learning and providing constructive feedback.We should explore hybrid intelligence, a partnership involving human intelligence and AI, to serve as a problem-solving ally for students. This will stimulate their quest for deeper understanding and develop creative, critical and system-thinking skills, in an increasingly technology-infused world.&nbsp;One way of motivating our pragmatic students is through formative assessment.&nbsp;A review of assessment tasks should serve at least two purposes:&nbsp;ensure that students cannot rely on GenAI to help them complete the assessment, although they can use it in the process;&nbsp;require students to apply key learning foci in as authentic contexts as possible, preferably in situations that require interactions with people and/or physical objects.&nbsp;Learning and assessment practices like these require demonstrating analytical understanding and should promote awareness of relationships in complex scenarios.&nbsp;Question 2: What is the value of formal institutional education?If students believe their learning can be outsourced to GenAI and other sources on the internet and, post-pandemic, prefer to learn and work at home, why should they come to campus for in-person lessons?For education reform, we must create enriching educational experiences on- and off-campus, which cannot be gained with human-technology interactions alone. Engaging in teamwork with their peers can teach students to navigate intellectual situations through verbal communication, body language, appropriate attitudes and other human behaviour.&nbsp;Think about the way teams are formed, too. GenAI can be used to engage students in interdisciplinary and transdisciplinary collaboration, helping them learn to solve complex problems by integrating contributions from each team member and offering disciplinary insights. Creating interdisciplinary learning scenarios can allow students to mitigate problems, negotiate positions, build character and promote diversity and inclusiveness.Question 3: How are HEIs addressing pressure points and roadblocks to stay relevant?&nbsp;Higher education institutions have their own educational challenges and pressure points. Some could be financial, such as budget constraints and affordability, while others are more process- and productivity-based. With the emergence of GenAI, institutions must address these pressure points rather than resorting to quick fixes. Doing so will not address the challenges we are facing or keep pace with rapid technological advancements.&nbsp;We need to stay relevant in a changing world. Universities need to prepare their graduates to adapt, whether that be in the workplace – perhaps in a job that does not even exist yet – or the next level of academic study and research. Forming partnerships with industry can help identify areas of disconnect across sectors and offer insight into ways to meet the demands of a technology-infused society.&nbsp;Adaptability and timely responses are crucial to remaining relevant. Teachers and non-academic staff need frequent upskilling and reskilling to keep up with the evolution of AI in the higher education landscape. Professional development can include working with GenAI to harness its full potential for education and employing learning analytics in impact and trace studies.&nbsp;Technology breakthroughs alert us to new realities – and the need for corresponding breakthroughs in higher education. Higher education institutions should accelerate transformation to adapt to escalating external changes. Courage, determination, a forward-thinking mindset, strategic vision and concrete action plans can turn pressure points into opportunities. As we venture into uncharted territory, those who dare to take bold steps will have the opportunity to reform, redefine and elevate higher education.&nbsp;Julia Chen is director of educational development at&nbsp;the&nbsp;Hong&nbsp;Kong&nbsp;Polytechnic University.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

The three questions that will define the future of education

Generative AI is here to stay – how can higher education respond? Julia Chen poses three fundamental questions to explore a strategic vision for the sector

The three questions that will define the future of higher education

The data landscape in higher education is as complex as in any major business in the world. Colleges and universities function as small towns, so to say that post-secondary education delivers services and offerings (from&nbsp;well-being and health to entertainment) to its customers – students – merely scratches the surface. Their operations cover admissions, course enrolment, degree awarding, student housing and athletics, among many others. Less obvious (and often overlooked) business processes include human resources, financials, budget, faculty affairs, research, enterprise resource planning, student services, dining, facilities and information technology.&nbsp;Such intricate functions generate monumental amounts of data to be integrated, managed and analysed. Universities therefore need a strategy and best practices in place, created in collaboration with the president, the business and information technology so systems work properly, planning can be effective and data-informed decisions can be made.&nbsp;This can be through, for example, IT or other executive initiatives from the president, institutional research, a chief data officer or executive of administration and finance.Why the ‘machine says no’: how asking for less can get you more…Campus webinar: Can universities keep pace with digital transformation?Eliminating harmful digital technologies in universities: a guideThis guide to data management in higher education outlines essential strategies and emphasises the evolution of data-management practices. The aim is to empower institutions to leverage data for better decision-making and operational efficiency.Before deciding how to manage data, it is first important to assess overall campus data maturity. Groups such as data management, data governance, strategic consulting or assessment can lead this process. This will assist in understanding the state of business intelligence and provide not only context but also point to areas to address first. For example, executive leadership might be asking about machine learning, predictive analytics or artificial intelligence, but it wouldn’t make sense to start there if there was not, for example, a central documented and governed data warehouse or data lake. While many resources are available that define data maturity,&nbsp;this image from Gartner is widely used.&nbsp;Once this initial assessment is completed, the real work can begin. The information below is presented in order, but note too that many activities are done simultaneously and one need not be finished before you move on to the next. In fact, most of these are never completely finished due to changes in technology, systems, leadership and initiatives.Establish data governanceData governance involves setting up regulations and policies compliant with standards such as, in the US, the&nbsp;Family Educational Rights and Privacy Act (FERPA) and the Health Insurance Portability and Accountability Act (HIPAA). Pivotal in managing and safeguarding data, data governance clarifies the roles within the data ecosystem, such as data trustees, data owners and data stewards, ensuring that everyone understands their responsibilities. Moreover, it involves mapping data across the enterprise to maintain data integrity and accessibility. Establishing effective data governance requires both executive support and grass-roots engagement to align with institutional goals and regulatory requirements.&nbsp;Define teams and strategy for integrations, data warehousing and reportingCentralised teams dedicated to integrations, data warehousing and reporting are essential for effective data management and analytics. Integrations focus on the seamless movement of data across systems. For example, if enrolment status is needed to be shared from the student information system (SIS) into the student housing system to determine eligibility, an integration would need to be created so the systems can “talk” to each other.&nbsp;For data warehousing, methodologies such as&nbsp;Kimball, Inmon or&nbsp;Data Vault, depending on the institution’s needs, ensure that data is stored securely and structured for analytics and reporting. The goal is to build a data warehouse that aligns with governance practices, supports reliable and valid data analytics, and creates a&nbsp;single source of truth.&nbsp;Lastly, establishing a business intelligence team can streamline the creation of analytical tools for data-informed decision-making.&nbsp;Create a data reference modelA data reference model (DRM) outlines the journey of data from ingestion to its use in downstream systems or tools. This model serves as a blueprint for the architecture of data systems to ensure efficiency and compliance with governance and security policies. Reference models from leading tech companies such as&nbsp;Microsoft, Amazon and&nbsp;Snowflake can serve as examples for creating an effective DRM tailored to an institution’s specific needs.Gain buy-in from stakeholdersBuy-in from faculty and administration and IT staff is critical for the success of data-management initiatives. This involves communicating the value and impact of proposed data strategies to all stakeholders from executive leadership and groups tasked with delivery. Engaging stakeholders in discussions early and often in the development process, as well as providing clear evidence of the benefits of data-management practices, are key strategies for gaining support.Continuously improveData management requires regular review and adaptation to meet evolving needs and to leverage new technologies. Mechanisms for continuous improvement, such as regular audits, feedback loops and training programmes, ensure that data-management practices remain effective and aligned with the institution’s strategic objectives. Establishing iterative approaches, such as Kaizen, PDCA or Kanban, encourages innovation and adaptability in the face of changing regulations, technologies and educational landscapes.&nbsp;Data management within higher education requires thought and strategy. Establishing robust data-management practices not only ensures operational efficiency but also enhances decision-making and enables the enterprise to effectively conduct business. By assessing data maturity, establishing data governance, creating centralised data teams and adopting a dynamic data reference model, institutions can remain agile in an ever-evolving technological landscape. Looking forward, advanced analytics and AI promise to revolutionise data usage in higher education, making the pursuit of data excellence a journey rather than a destination.Nick Chaviano is director of data services at Georgia Institute of Technology.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

Data management in higher education

By assessing data maturity, establishing data governance, creating centralised data teams and adopting a dynamic data reference model, institutions can remain agile in an evolving technological landscape, writes Nick Chaviano

Data management strategy in higher education: a blueprint for excellence

The investment that universities are making in digital projects is huge. While many of these initiatives are necessary after&nbsp;years of underinvestment, large claims are often made about the solutions that the investment will bring.&nbsp;Such promises often do not ring true for many who have worked in higher education over the decades. This prompts the question to be asked about what has been learned from the many years of digital transformation attempts in HE, before, during and after the online pivot of 2020.&nbsp;That is what this series of resources by academics from across the UK sector aims to explore. The writing team is: Sarah Dyer, associate dean for teaching and learning at the University of Manchester; Craig Walker, director of HEdWay Group; Lisa Harris, director of digital learning at the University of Exeter Business School; Arunangsu Chatterjee, dean of digital transformation at the University of Leeds; and Niamh Downing, pro vice-chancellor (education and student experience) at Canterbury Christ Church University.&nbsp;In our view, digital transformation must be viewed less as a series of technology projects and more as a state of perpetual agility and continuous digital service improvement. It should be ready to evolve for whatever our stakeholders need. For example, effective digital transformation requires re-evaluation of business case methodologies and approaches to communications internally and with external stakeholders, following work by Andrew Greenway, Ben Terrett, Mike Bracken and Tom Loosemore. The development of an extensive engagement plan can enable empowered conversations, the evolution of a shared view and appreciation of the complexity of the operating space.In this series of articles, we present evidence from multiple universities of the seeds of digital transformation. Collecting and sharing approaches and learning is important to avoid reinventing the wheel. We draw on both published work and on conversations with those who were involved at the time. These case studies demonstrate the true extent of the organisational challenges in realising the opportunities of digital transformation. For example, we show how Imperial College London established and embedded a way of working in collaboration with students that reframed not only who was involved in decision-making but also the kinds of conversations that were had.&nbsp;We present these examples within a framework of what we consider to be “ideas that make a difference” for effective digital transformation.&nbsp;In identifying these ideas, we have sought what could be thought of as&nbsp;threshold concepts for digital transformation of universities. These may be somewhat counterintuitive and troublesome ideas, particularly when viewed in the context of current practice. We believe that appreciating these ideas is transformative in itself, changing how people think about digital transformation and the possibilities for the future of universities. As is the case with threshold concepts these ideas may also prompt people to rethink their own role in the process of transformation, and the impact transformation may have on their own working life and identity.&nbsp;The “ideas that make a difference” include that:&nbsp;asking for less can get you morecollaboration isn’t the same as hoping not to annoy anyone&nbsp;use authentic stories to capture the “unheard voices”.&nbsp;We intend these ideas to catalyse changes to how universities think about and approach the digital agenda,&nbsp;but they are also transferable to other contexts in a way that facilitates meaningful and sustainable change in higher education.&nbsp; We are interested to hear if these ideas resonate with you, and what other ideas we should be talking about, too.Sarah Dyer is associate dean for teaching and learning at the University of Manchester.Craig Walker is director of HEdWay Group.Lisa Harris is director of digital learning at the University of Exeter Business School.Arunangsu Chatterjee is dean of digital transformation at the University of Leeds.Niamh Downing is pro vice-chancellor (education and student experience) at Canterbury Christ Church University.&nbsp;Sarah Dyer, Craig Walker and Lisa Harris will be among the speakers at&nbsp;Digital Universities UK on 16-18 April at the University of Exeter. Their session is “Ideas that make a real difference”.&nbsp;If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

Ideas to shape the digital transformation of universities

In a series of Campus resources, UK digital adopters explore what has worked (and what has not) as universities go digital and where investment in technology can be most effective

Ideas that make a difference for the digital transformation of universities

Two years ago, and after two decades spent working in the non-profit sector and for technology firms, including Google and Twitter, I&nbsp;made a move into higher education to advance a mission that had been quietly building in me during my years as an entrepreneur and executive – a mission centred on evangelising the humanities.Months after joining the faculty of Virginia Tech, I wondered: if breakthrough leaders of our AI-ascendant era will, above all else, need to embody high human skills and sensibilities, why isn’t there an executive degree in the humanities in the HE landscape?&nbsp;A year later, in 2023, I&nbsp;launched (and lead) the Virginia Tech Institute for Leadership in Technology.&nbsp;The institute seeks to draw on the humanities – from history and religion to literature and creative writing – to offer rising leaders in the technology landscape a new way to cultivate their leadership.&nbsp;How authentic stories can empower the ‘unheard voices’Campus webinar: Can universities keep pace with digital transformation?Biased AI poses a threat to academic freedom that must be confrontedOur inaugural class of fellows includes executives from Fortune 500 companies such as Amazon, Microsoft and Boeing. While much of the coursework in our one-year low-residency programme takes place virtually, our cohort gathers in person three times per year. There’s an ignition experience in Blacksburg in the fall, a January immersion in a place known for its commitment to the arts (this past year in Miami) and a celebration of community in the D.C. area this April. Before receiving their leadership credentials, fellows will have together studied history and philosophy, written about literature and ethics and immersed themselves in place and faith.A lot of the debates about the future of technology tend to be about policy and rules and controls and governance. For me, though, other animating questions are: who’s in the room where things happen? What kinds of experiences do people bring to those rooms? Have they lived in specialised silos – or have they, in the best tradition of the humanities, lived boldly in the human other?&nbsp;Why the human element matters in tech leadershipI am often asked: what are the humanities? Education and experiences that cultivate the skills of introspection and imagination, storytelling and story listening, I often say. A year ago, I crafted a six-word poem to express the power of the humanities: Awe and wonder, in the other. In our self-involved, short-form era, the ability to think selflessly, and in long form, about human communities near and far – past, present and future – not only forms the basis of humanistic enquiry, but also constitutes the essence of leadership.When selling software to customers, designing services for new markets, imagining product market fit or developing an approach to recruiting and retention, the ability to live in the human other is, quite simply, a superpower.I came to understand the importance of humanities during my career not long after reading&nbsp;The Fuzzy and the Techie: Why the Liberal Arts Will Rule the Digital World by Scott Hartley, a former colleague from Google. Many leaders in the technology sector bring to bear a foundational understanding of the technical underpinnings of our technology landscape – but, in my experience, the most inspired also possess a distinct human element that we should cultivate and celebrate.Blending humanities with STEMIn his book, Hartley offers a multitude of real-world examples that lift the power of blending liberal arts skill sets with the best of STEM, including data literacy. He cites examples that go well beyond Steve Jobs’ assertion that Apple aspires to “stand at the intersection of technology and the humanities”. Indeed, blending the liberal arts and STEM has enabled leaders beyond Jobs – in fields from national security to gene editing – to use their intuition in balancing quantitative data with qualitative assumptions, to draw on their sense of narrative to detect signal from noise, and to build on their familiarity with varied places, cultures and norms to adeptly expand businesses internationally.My own career, like those of so many across the technology landscape, was fuelled by the humanities, too – across communities and countries, fields and functions. Until I read Hartley’s book, I lacked the language to see this fuel. Afterwards, as Bruce Springsteen sings, I “gave it a name”. I became active in the humanities ecosystem, joining boards such as Virginia Humanities and the National Humanities Center, and encouraging other leaders in the liberal arts field to consider&nbsp;“going to market” in new and novel ways. Our technology landscape – indeed, our culture and civic spaces, writ large, I felt – depended on thinking differently.‘The power of the liberal arts in inventing tomorrow’In 2022, Sylvester Johnson, associate vice-provost for public interest technology and founding director of the Center for Humanities, asked that I consider Virginia Tech as a stage on which to advance my thinking and mission. Truth be told, he had me at hello. To represent the power of the liberal arts at a storied research university dedicated to inventing tomorrow and inspired by the past – and a timeless motto, “Ut Prosim” (“That I May Serve”) – inspired awe and wonder in me.&nbsp;We’re honoured to have launched the&nbsp;Institute for Leadership in Technology here in Blacksburg. We’re even more grateful to our&nbsp;inaugural class of 2024, which embodies the best of the global leadership this new era will demand. And we’re confident that, over time, we will extend all we’re learning about the links between the humanities and leadership to other segments of our society, not just technology.Most of all, we hope our mission will move the conversation forward and catalyse other efforts, here and abroad, that lift the humanities along with all the other elements of human flourishing.Rishi Jaitly is a professor of practice and distinguished humanities fellow in the Center for Humanities, where he founded and leads the Institute for Leadership and Technology, at Virginia Tech.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

Amid a growing awareness that humanities students and faculty need data literacy, Rishi Jaitly, a longstanding champion of the humanities in technology, explains why data and technology leaders also need an education in the liberal arts

Harnessing the humanities for transformative tech leadership

A survey of more than 2,000 global decision-makers and employers, commissioned by the data visualisation company Tableau, found the not-exactly-shocking result that&nbsp;82 per cent of leaders expected employees to already have basic data literacy skills.In today’s rapidly evolving digital landscape, the ability to interpret and analyse data is no longer just a valuable skill, but an imperative to remain competitive, both internally and externally. Employees can now leverage data-centric technologies, automation and artificial intelligence (AI) to support and add valuable insights to their work. Studies have revealed that the arrival of generative AI has led to&nbsp;worker performance improving by as much as 40 per cent compared with those who do not use it.The what, why and how of data visualisation for higher educationImproving students’ digital literacy for remote learningDIY learning analytics: using data to improve online teachingWith digital technologies redefining the modern workplace, universities must be proactive. We need to reshape teaching methods and curricula to support students and address the escalating demand for fundamental data literacy skills by employers.What is the best approach to increasing data literacy in a university setting?The notion of data literacy is multi-faceted and constitutes several key non-technical and technical skills.&nbsp;Individuals must possess critical thinking skills when attempting to interpret data, but harnessing technical skills such as data analysis and data visualisation can also be valuable. These skills empower people to delve deeper and discover insights that will enhance their work.There are many ways to teach technical skills, such as releasing self-study course material, lectures and online video materials. As part of the&nbsp;LSE Digital Skills Lab, a dedicated team on campus committed to empowering both students and staff with essential digital skills, we experimented with the former. However, we discovered this approach yielded significantly low completion rates and hindered the full realisation of the value these courses offer. We recommend following a practical learning approach. Here is our advice:Hosting practical workshopsPractical workshops independent of university modules can be curated to introduce new skills and concepts to university students and staff in a slow and controlled environment. The course content should be organised into a series of practical tasks, where complex concepts are broken down into manageable chunks for learners to complete in a single session. Each task should be designed to introduce new aspects of the software to learners, building on knowledge gained from previous tasks.Dedicated staff should be readily available to provide guidance, ensuring learners execute tasks correctly and efficiently. Having dedicated staff present will also offer learners the opportunity to address any additional questions or concerns they might have about the task or software. It is important to foster a conducive learning environment and give learners the flexibility to work at their own pace – this will enable them to revisit tasks as needed and fully engage with the content.Running frequent practical sessions is important to reinforce learning. It allows learners to capitalise on new knowledge gained while still being readily accessible in their memories. Since adopting this approach, we have consistently achieved a near 100 per cent satisfaction rate, with most learners feeling very confident in their new skills.Cross-department collaborationAnother effective method of bringing data literacy to the forefront of the university curriculum is through direct collaboration with departments. Students can coordinate with course convenors and realise a practical application to aspects of their coursess, providing them with seamless experiences and maximising their benefit from such courses.Working with the&nbsp;LSE Department of Statistics to translate theory into practice, the Digital Skills Lab introduced a bespoke course this year designed with statistics students in mind. It allowed them to apply the concepts learned in lectures, such as descriptive statistics, regressions and hypothesis testing. Courses such as these supplement already enriching modules by adding another layer to the learning of students, helping them to consolidate their understanding of the theory.Managing challengesIntegrating data literacy into the curriculum presents its own set of challenges. When introducing practical concepts into the curriculum, we have to make sure the skills gained are of a suitable level and meet requirements for employers. Holding discussions with alumni and employers is a great first step towards creating meaningful content.&nbsp;Another issue arises, however, in managing the dispersion of existing data literacy in students. Some students might have already had the privilege of being exposed to certain applications and programming languages, presenting an uneven playing field. Offering pre-sessional workshops can create a floor level of base knowledge, which can be built on from then.Finally, how can traditional course leaders, who perhaps prefer to stay the course, be convinced to move away from theory and integrate practical aspects into their teaching? These challenges risk hindering the development of data literacy and must be tackled head on. We have found one of the best ways to tackle this is through transparent dialogue and demonstrating the benefits of practical integration into courses, through examples of past projects.A basic level of data literacy is evolving from a luxury into a norm in workplaces, creating a need for students to be equipped with the necessary skills to tackle the challenges of data analysis head on. By focusing on a practical style of teaching, breaking down tasks progressively and consistently, and collaborating with departments to bring forward practical applications to the theory they teach, these challenges can be overcome.Ermal Haranlli, Nedelin Velikov and Kehara Warnakulasuriya are part of the Digital Skills Lab at the London School of Economics and Political Science.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

Prepare tomorrow's workforce by integrating data literacy

Data literacy skills are increasingly important in the modern workplace. Ermal Haranlli, Nedelin Velikov and Kehara Warnakulasuriya offer their advice on readying your students for the future

Prepare the workforce of tomorrow by integrating data literacy into your curriculum

A question students regularly ask me is: will AI replace human workers? I imagine many educators may be experiencing the same thing, in particular, those teaching AI or business analytics-related programmes and modules. This is not a surprise. AI, in particular generative AI, continues to be a hot topic and is becoming more prevalent in all major industries, including education. AI could automate 10-30 per cent of jobs in the UK, and more than 75 per cent of companies globally intend to adopt AI and related technologies in the next five years.&nbsp;Going back to the question, a common view is that AI will change the employment landscape. It will replace some professions and create new jobs, but it will not replace humans. Human workers, nevertheless, need to upskill and adapt to work with AI and machines. Many articles offer their interpretations of the key AI skills that professionals need to learn and develop. It then becomes clear that university students need to develop AI literacy skills for the future workforces. AI literacy has already been considered a key graduate attribute for university students.What is AI literacy?Building from and developing upon digital literacy, AI literacy is more complex and requires users to have some fundamental understanding of technical knowledge – ie, a basic idea of how AI works, what AI does and where AI tools can be applied, and the potential social and ethical implications of AI applications. In the context of generative AI, ethics and fact-checking are particularly important, because it can create new text-, video- or audio-based materials and other creative artefacts with ease. However, we are unclear about current students’ AI literacy level, even though it has been reported that some students have used generative AI tools to help them with their studies or assessments. Therefore, how can we, educators and universities, help and support students to move from A, that is, their current state of knowledge, to B, that is to become industry-oriented AI literates? The answer is AI literacy education.&nbsp;Resource collection: AI transformers like ChatGPT are here, so what next?Transition from expert to guide by embracing generative AIThree ways to use ChatGPT to enhance students’ critical thinking in the classroomHow can we teach AI literacy?As the name suggests, AI literacy education refers to co-curricular or extracurricular modules or courses, offered by universities, and designed specifically to develop students’ AI (including generative AI) literacy skills. These courses and modules are aimed at undergraduate and/or postgraduate students, and can be delivered at the institutional, faculty or departmental level. The University of California, for example, designed a multidisciplinary campus-wide AI curriculum for undergraduate students. It focuses on developing students’ technical and ethical skills. City, University of London introduced a similar type of AI course for postgraduate students studying radiology.&nbsp;This is clearly where we should be heading, but there is a shortage of guidance and practical examples on AI literacy curriculum design and pedagogical approaches, and in particular, discipline-specific strategies for enhancing student engagement and learning within particular subject disciplines. As a result, some important questions remain unanswered. For instance:&nbsp;How should university and academic tutors design and develop AI literacy courses for specific subject disciplines?&nbsp;What types of AI literacy skills should be included in this course?&nbsp;What teaching approaches are most effective in teaching AI literacy skills?AI literacy course design&nbsp;The questions raised above are mostly related to curriculum design. If universities consider offering an AI literacy course to students, the following recommendations may be helpful:First, it is necessary to understand students’ current AI literacy level so that the course can better address the knowledge and skill gaps and meet student expectations. You can gain an understanding of current levels by conducting an AI literacy survey. You can then use the AI literacy framework, developed by Davy Tsz Kit Ng, Jac Ka Lok Leung, Samuel Kai Wah Chu and Maggie Shen Qiao as an underpinning framework to develop an AI literacy course. This framework includes four dimensions:Knowledge and understanding of AIUse and application of AIEvaluation and creation of AIAI ethics&nbsp;It also suggests the essential AI skills (both technical and non-technical) that need to be covered in the course.&nbsp;Third, tutors need to consider discipline-appropriate approaches to teaching AI literacy skills. Innovative approaches such as gamification, simulations and project-based learning have proved to be effective.&nbsp;And finally, the skills covered in the curriculum need to be industry-oriented. Students need plenty of hands-on opportunities to use AI tools to solve problems and deal with challenges that they may encounter in their future workforce. For example, in one of my modules, I worked with more than 300 students at King’s Business School to create a rule-based chatbot as an additional assessment support tool. Through participating in the project, students learned about the advantages and challenges of using a chatbot in customer support, and how to create such a chatbot using Google Dialogflow. They were also able to test out innovative ideas to improve the chatbot’s efficiency.The global workforce stands on the threshold of a major change in its composition. Universities are still unsure of how to deal with these technologies and how to incorporate them into teaching and learning. However, the technology isn’t standing still, and neither is industry; AI is being adopted and incorporated into everyday working operations and this process is increasing almost daily. Universities have made positive strides in improving digital literacy for graduates, but now they need to do the same for AI literacy.&nbsp;Christine O’Dea is a senior lecturer in management and technology education at King’s Business School, King’s College London. Michael O’Dea is a lecturer in computer science at the University of York.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

How can we teach AI literacy skills?

Universities have made positive strides in improving digital literacy for graduates, but now they need to do the same for AI literacy

Storytelling is a meaningful activity that we all do. We all want to hear each other’s stories. It’s a fundamental aspect of human communication.We remember the stories we are told. We identify with them and are intrigued. We wonder what else there is to know. Stories can inspire us, frighten us or challenge us to take action.Franklin D. Roosevelt famously used storytelling to build trust and support for his policies. With his “fireside chats” – radio broadcasts that conveyed complex issues in a conversational manner – the US president during the Great Depression and the Second World War connected with the general public.Organisations often turn to storytelling because it engages people on an emotional level much more than statistics ever can. If consumers share a brand story with their personal networks, it lends greater value and authenticity than standard marketing communications can ever achieve.Why the ‘machine says no’: how asking for less can get you more…Transition from expert to guide by embracing generative AIHow small universities can make big digital changesMore fundamentally, storytelling can be a powerful means of generating wider acceptance of difficult culture-change projects. Stories can provide a way for leaders to communicate openly and authentically, fostering trust among employees. When people understand the context and reasons behind the change, they are more likely to trust the leadership and the process. Stories can open up a dialogue; they provide a platform for discussions, questions and feedback, creating a more interactive and inclusive environment.But is it really that simple?When proposing change, leadership needs people to take notice of the communication and to respond accordingly. This is particularly important when we consider the contemporary working environment within universities. Decision-makers often jump from online meeting to online meeting, multitasking where they can during presentations to get rid of multiple pinging chat messages or a stream of emails. In such environments, storytelling clearly has a role in getting important messages through the clutter and making appropriate action more likely to be taken.But what if the audience interprets the “story” in multiple ways? Or what if the credibility of the storyteller is compromised in some way? How might leadership’s “official story” be challenged and changed by others with “alternative stories”?In the next section, we apply storytelling to the challenges of digital transformation. We draw on brief practical examples that offer different perspectives on successful storytelling. They demonstrate that change requires a lot more than a fireside chat, and stories of lived experience from a range of stakeholders can result in better-quality outcomes.How storytelling can be an effective catalyst for changeThe University of Exeter’s digital team use “stories” to help ensure effective communication. Rather than rely on a lengthy slide deck, they talk informally with distinctive props that are not only memorable but make people more inclined to share the associated key messages.Internally, this method helps to cut across silos and improve basic communication about what the digital strategy is and why it is important. Externally, the approach has enhanced network building and helped to build a reputation. For example, a recent conference presentation “Telling the story of the stories” resulted in one delegate publicly claiming it was “the best five minutes of the conference”.Small grants have been made available at the University of Manchester to support educators to undertake person-centred research. The tools they employ – empathy maps, journey maps, systems maps and prototype testing – surface the holistic, human and emotional experiences of what is being investigated. When asked to evaluate success, grant holders identified the collection of stories from students to share with department colleagues in making the case for change. These were stories of the lived experience of a particular institutional problem – for example, poor timetabling or missing accessibility support. They proved to be far more powerful in prompting action from decision-makers than quantitative data or abstract arguments.At Imperial College, a student complaint in 2016, carefully curated with the assistance of the students’ union, became the totemic catalyst for the director of timetabling to redesign and then digitise examinations timetabling processes to ensure high-quality exam conditions for every student in the future. Phrases such as “drenched in my own sweat” and “my head was spinning” were the essence of the student’s complaint. These were used across dozens of teams with a rawness and authenticity that caused people to put aside their own local processes and design a new student-informed, data-led exam timetabling service that was better for everybody.Finally, an enthusiastic group of University of Exeter students were instrumental in inspiring change during the Covid-19 pandemic. Their Auditio blog told the stories of staff and fellow students who had addressed challenges of learning and working online during lockdown. In their own words: “Auditio means ‘to listen’ in Latin. That is exactly what this blog is about. We want to share your stories of authentic experiences, learning achievements and all the challenging things along the way.” The personal anecdotes that the students curated and published were powerful in demonstrating that cultural change is not just a top-down initiative but something that everyone can actively contribute to – including, in this case, the “customers” themselves.What transferable lessons can we glean from these examples?1. Use a small number of totemic or memorable narratives to engage people and elicit their stories.2. Find ways to get “hard-hitting” stories from the people who never turn up when needed but who always complain if their problem persists. This means actively seeking out and engaging those affected to curate the story from the input of all stakeholders.3. Involve all stakeholders in developing the solutions. This means getting them to make the sketches, drawings, wire frames or messy prototypes in order that users shape the solutions.Craig Walker is a director of HEdway Group. Lisa&nbsp;Harris is director of digital learning at University of Exeter Business School. Sarah&nbsp;Dyer is associate dean for teaching and learning at the University of Manchester.Sarah Dyer, Craig Walker and Lisa Harris will be among the speakers at&nbsp;Digital Universities UK on 16-18 April at the University of Exeter. Their session is “Ideas that make a real difference”.&nbsp;If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

Using storytelling to drive digital transformation

Storytelling can be a powerful change agent. When proposing change – such as the digital transformation in universities – leadership needs to communicate in ways that cut through the noise and inspire action, write three digital adopters

How authentic stories can empower the ‘unheard voices’

As we set about bringing change in various institutions, many of us have been warned against “stepping on toes” during what were ostensibly encouraging conversations. It’s a perplexing warning at first – perhaps incongruous, perhaps slightly unsettling –&nbsp;but ultimately it’s the kind that drains energy and ambition. Whose toes might you be about to step on and by doing what? Will it be clear if it’s about to happen or will you only know in retrospect? Is there another way to do things that doesn’t endanger any toes?&nbsp;The feeling that this all engenders, and our near-ubiquitous experience of it, prompts us to feel it is a sentiment worth unpacking. “Don’t step on anyone’s toes” signals many things about universities. It reveals something of the lived reality of working in organisations as large and complex as these, places&nbsp;in which accountability and responsibility are often distributed, sometimes abrogated, and might not map neatly on to any organisational chart. Most universities are decades, if not centuries old. They carry their history with them in legacy organisational structures, the role definitions (and titles), as well as mindsets, skill sets and ways of working.To get things done in universities, do we need to embrace conflict? Should we not worry about stepping on toes? Discussing a not dissimilar organisation, the UK civil service, Andrew Greenway, Ben Terrett, Mike Bracken and Tom Loosemore note in their 2021 book,&nbsp;Digital Transformation at Scale, that “the major determinant of long-term personal success” in the civil service is the ability to “keep your colleagues close” and therefore “if there is an explicit argument or disagreement, that is a problem in and of itself”.&nbsp;“Successful digital leaders need to embrace a degree of conflict – as well as the ability to collaborate – in order to unblock institutional issues that prevent change from happening,” they write. “In doing so, they are effectively pencilling their resignation letters from day 1 within a culture that places little value in bringing issues to a head, nor gives incentives to do so.”How to turn an average collaboration into a dream teamAdvice for effective cross-team collaboration for researchFive tips for building healthy academic collaborationsUnlike civil servants, many academics relish a degree of intellectual conflict, and yet universities seem ill-equipped to productively handle conflict. Some people want to argue each point from first principles and the argument can seem like the end-point in itself – not a stage in coming up with a better way of doing things. Arguably, a persistent distaste for formal management leads to the neglect of structures and skills that mediate conflict and some pretty shoddy practices passing as management.&nbsp;Universities are not dissimilar to many organisations in that people who end up making decisions – or interpreting what decisions will mean in practice –&nbsp;have been successful within the status quo. What might be unusual about universities is that those people may have been successful as researchers rather than managers:&nbsp;building exquisitely detailed expertise and insight about a given topic, but at the expense of a diverse breadth of experience and responsibilities. Moreover, we would argue that one of the consequences of the lack of clarity about responsibility and accountability is the persistence of organisational cultures that cling to indefensible hierarchies such as those between academic and professional service staff and research and education work. While universities would no doubt benefit if they were to evolve to be organisations that could better manage conflict, we suggest that, in the here and now, we must develop strategies for collaboration.&nbsp;Understanding that collaboration needs to be proactively designed and nurtured is, we think, an idea that makes a difference for digital transformation. Collaboration requires skills such as the ability to work with people who have different expertise and experience, and the ability to accept and process creative tensions as a group (this is partly about trust and relatability, which may be at an all-time low in HE). These skills will be developed through practice and will benefit from good facilitation.&nbsp;There are many possible approaches, but in this blog we highlight prototyping as a tool to support collaboration.How prototyping can be used to improve processesA prototype is a low-fidelity rendition of a possible solution. It acts as a boundary object between different expertise and different needs, enabling people to collectively deepen their shared understanding. Prototypes externalise ideas in ways that facilitate conversations about possible solutions and allow people to play with and iterate ideas in a low-stakes and psychologically safe environment.&nbsp;As an example, Imperial College London used prototyping to improve timetabling of exams at a time when it was felt there weren’t enough places in exam halls. While the status quo of devolved responsibility was familiar and had department confidence, it also meant the timetable was built as a collection of locally written timetables. Some students would therefore sit exams in halls that weren’t full, while others would be in rooms that no one would choose to sit an exam in. The cross-department working group wrote a proposed re-rooming protocol with volunteers. They then modelled the approach using a visual prototype to illustrate how it would work and the change in outcomes.

  
      
  
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&nbsp;Participation in the prototyping process gave timetablers and department academic leaders the confidence to pilot re-rooming at scale for the next examination cycle. The net result was that more than 8,200 students sat examination papers in better conditions than they otherwise would have, with the work of the extended team recognised by university awards. These results catalysed a stream of ongoing service enhancements, including development of digital approaches for collecting requirements and modelling timetables.What does it mean in practice?Digital transformation requires us to build organisations that support productive collaboration. Here are ways we suggest we might begin to do that:If you hear someone telling you not to step on anyone’s toes, pay attention. If you notice in the moment, ask them to say a little bit more about that: what kind of thing are they thinking about? Has that happened in the past? What do they think are the dangers of “stepping on toes”? And what are the costs of working with that intention?Allow time in project schedules and processes to build relationships and to agree ways of working.Actively create an inclusive culture in which people can challenge hierarchies without fear of repercussions.Use facilitation tools to invite diversity of expertise and perspective into the process.Use prototypes as a way to allow people to draw on their own experience and expertise to assess possibilities across specialisms. The This is Service Design Doing methods library is a great place to explore this approach.&nbsp;Sarah Dyer is an associate dean for teaching and learning at the University of Manchester. Lisa Harris is director of digital learning at University of Exeter Business School. Craig Walker is a director of HEdway Group.Sarah Dyer, Craig Walker and Lisa Harris will be among the speakers at&nbsp;Digital Universities UK on 16-18 April at the University of Exeter. Their session is “Ideas that make a real difference”. &nbsp;If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

Collaboration requires more than not stepping on toes

To get things done in universities, do we need to embrace conflict or find new ways to work together? Here, three digital adopters explain why collaboration isn’t the same as hoping not to annoy anyone 

We increasingly experience quality affordable digital solutions in everyday life. The best evolve, with improvements and added features in response to user feedback, and we generally don’t even ask for them. Providers just make technology better for us.&nbsp;Yet the user experience is often so frustrating in higher education. Requests that seem reasonable take longer and longer to service. Technology gets harder, not easier, to use, and “machine says no” is increasingly the norm…Why?Foremost, resources and time don’t stretch to everything we need to do, let alone what we would want to do. For example, it should be possible to assemble a complete digital picture of any individual student’s engagement with educators, learning resources, the campus and their peers. Yet, in many places, even finding out who attended a given class is hard.&nbsp;Find more resources on digital transformationUse technology to boost productivity and beat burnoutLearning the machine: shaping the future of creative educationThe causes of this gap start with under-investment and lack of future-proofing core systems. For many things being asked of our systems now, technology dead ends are the result of too many poor decisions often taken years, if not decades, in the past. Compounding the challenge is the increasingly urgent need for new functionality – to enable, for example, multiple intakes for trans-border education or better support for hybrid learning. The net effect is that demand for new digital solutions is outstripping capacity to deliver, exacerbated by legacy systems that were never intended to support such functions.&nbsp;So schools and directorates end up in an internal competition of sorts for investment in digital, where the rules might not be explicit and a decision to invest is a triumph in itself. Before projects even start the stakes are high. When investment is secured, delivery is limited by budget and time. We then cram in as many “requirements” as we can because experience shows that follow-on investment rarely...well, follows on.To complicate matters, fragmented systems and data, patchworks of integrations and bespoke dashboards assemble a “nearly good enough” experience for now. Scratching the surface, however, exposes the underlying fragility. Few, if any, people know how the whole edifice works, and data quality is nobody’s responsibility. Adding functionality is fraught. And although institutional data joining, reconciling, being discoverable and being useful are readily talked about, these are fiendishly difficult to achieve in practice.Technology delivery teams face an intractable dilemma: a torrent of need for digital now that can’t possibly be met with the resources and legacy fragilities they have to work with. Needs that should be fast to fulfil turn out to be prohibitively expensive or even infeasible. Examples include January student intakes, stackable credits and international education partnerships. All these have consequences for student record and finance systems that were never designed to deal with what would be needed.&nbsp;Perhaps inevitably, each new ask is “strategically urgent”, which never allows enough time to re-engineer the core. The technology teams then add yet another layer of workarounds at the expense of further weakening the technology underpinnings.As well as draining scarce technical capacity, every added layer makes even minor changes increasingly onerous and sapping of time, energy and will.&nbsp;“The machine says no” because the machinery is fundamentally broken. The legacy of technical choices made years and sometimes decades ago drastically limits what can be achieved today, driving up cost and driving down delivery.So what should universities do?First and foremost, institutions need to recognise the constraints of the legacy systems and start doing something about it. They could re-imagine and reframe what higher education needs from technology – not just now but for the foreseeable future. Ideas such as re-composable curricula that&nbsp;Swansea University Digital Strategy is working towards will answer the questions needed to rebuild core systems for the future. This will probably take years but will yield big quality and productivity gains in the medium term.&nbsp;“We are lucky at Swansea to have a leadership team that recognises both the challenge and the urgency of the task. We are all working to rethink our approach to learning and research. To make that happen continuous improvements, not silver bullets, are the key. This is a job for the whole university and, yes, financing is a challenge, but imagination is what we really need,” said Paul Westmore, the university’s chief digital and information officer.Second, lower the stakes for everybody. Stop demanding new systems now and instead commit to people-centred incremental improvements to services and processes that improve the user experience. And, most importantly, answer the detailed questions in policy that enable digitisation. For example, Imperial College London’s&nbsp;people-centred policy work resulted in a digital-ready&nbsp;academic timetabling policy that unlocked the path to personalised academic timetables on devices of choice for students, and improved the annual process for academics, without needing a new timetabling system.&nbsp;“Delivering more should not always be a huge shift for an institution, rather another step on a journey where people, processes and policies are the pathway. Mouldy bread will never deliver golden toast, regardless of how fancy your toaster is. Always start with the baker!” said Helena Smith, director of timetabling at Imperial.Finally, build digital alliances that bring together academic, policy, operational, data and technology experts into multi-expertise, domain-aligned teams with the remit and budgetary authority to drive digitisation across whole domains such as education, student experience, research, estate, people, money and governance.&nbsp;Digital is a lot more than the underpinning technology. Successful institutions will be the ones that do the hard work in policy, service design and building users’ digital confidence. With the right foundations, technology teams will have the conditions they need to develop the digital solutions that our educators and researchers crave, and that our students deserve.Craig Walker is a director of HEdway Group. Lisa Harris is director of digital learning at University of Exeter Business School. Sarah Dyer is associate dean for teaching and learning at the University of Manchester.Sarah Dyer, Craig Walker and Lisa Harris will be among the speakers at&nbsp;Digital Universities UK on 16-18 April at the University of Exeter. Their session is “Ideas that make a real difference”.&nbsp;If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

Why asking for less tech can get universities further ahead

Under-investment and lack of future proofing of core digital systems have left many universities with creaking legacy infrastructure that cannot support increasing demands. Here, three digital adopters suggest that incremental improvements could be a way forward

Why the ‘machine says no’: how asking for less can get you more…

Higher education institutions are always looking for innovative ways to provide enriching learning experiences. Some use telepresence to do this, arguing that it enhances the interaction between students and their teachers.&nbsp;What is telepresence?&nbsp;Telepresence combines holographic technology with online collaboration tools to create a unique learning environment. It allows an individual to execute actions in a remote or virtual setting as if they were physically present.This semester, we incorporated telepresence, in collaboration with two colleagues from different regions, into one of our courses. In this resource, we identify several advantages to this approach.&nbsp;1. A greater sense of intimacy&nbsp;The use of holographic technology brings people closer than traditional video conferencing. It can help students feel as though they are in the same room with their professors, even if they are in a completely different country. It comes as no surprise that this significantly improves communication and comprehension, evidence of which we saw when we carried out surveys at the end of the course.2. Students benefit from diverse teaching stylesOne of the main advantages of telepresence is the ability to connect students with teachers from different regions. This means they have the opportunity to experience an array of teaching styles and perspectives, greatly enriching the learning experience and broadening students’ horizons.Thanks to the fact that our course’s learning objectives and competencies are clearly established, teachers with very diverse experience and perspectives can contribute without jeopardising its integrity. Our collaboration allowed students to be taught by three different teachers in a five-week course. In addition to our usual tutor, we had a professor with extensive industry experience who shared insight into his professional practice and another with a more academic background who was well placed to share perspectives on and analysis of theoretical models. Each had a different style of engaging with students.3. Students can interact with others from different regions&nbsp;In addition to being connected to professors from different campuses across Monterrey Institute of Technology, students on our course were also able to interact with peers from other regions in Mexico by taking part in contests or activities facilitated by collaboration tools. We used Socrative, a platform that allows users in different locations to compete against each other in races in real time. Each campus had a “racer” that advanced when a member of their team answered a question correctly. This enhanced cultural awareness and ideas exchange, which further enriched students’ learning experiences.&nbsp;Feedback from students participating in this training unit indicated that they highly valued interaction with their peers from other campuses, as forming meaningful connections is an essential part of the university experience. Telepresence breaks down geographical barriers and creates a global learning environment.Resource collection: Making online nearning funSerious play: boosting engagement in online courses through gamesSparking online joy: five ways to keep students engaged4. It facilitates the use of online collaboration toolsThe use of telepresence technology comes hand in hand with the use of other online collaboration tools that facilitate interaction and teamwork, such as Menti, Kahoot and Padlet. Since telepresence makes teachers seem as though they are in the same room at once, they can moderate contests in multiple classrooms simultaneously. Students can log into these platforms and monitor progress on their phones or laptops or a screen in the classroom. They can also share documents, conduct real-time surveys, participate in online discussions and collaborate on projects.&nbsp;5. It promotes healthy competitionTelepresence facilitates activities that promote healthy competition between students in different participating regions or campuses. Cross-regional challenges can inspire students to work harder and help them bond with other team members.Implementing telepresence&nbsp;Introducing telepresence into the classroom brings varied benefits. However, there are some challenges that one must keep in mind to implement it successfully.&nbsp;Paradigm shift: as educators, we are now used to talking to a screen, but the feeling of being broadcasted as a hologram remotely might be daunting.&nbsp;Infrastructure: as with any technology-focused innovation, having access to the right tools and technology is key.&nbsp;Training: learning how to use the technology is a fundamental aspect. You need to know how to use the interactive elements, how to manage different screens from the broadcasting room and how to manage a class with students in different locations so that everyone feels included.Student involvement: educators must be able to create an atmosphere in which students feel motivated to learn and confident about participating and interacting with peers in other locations. Where possible, refer to students by name. Ensure you explain the benefits of working with students in different locations and prepare well for the sessions so you can answer any questions and address any concerns.&nbsp;Implementing cutting-edge technology enriches the learning experiences and prepares students for an increasingly interconnected and diverse world. By connecting students with teachers and peers from different regions, telepresence is transforming the way we teach and enriching the learning experience.&nbsp;Sidney Ornelas is a professor at Tecnologico de Monterrey.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

Using telepresence to enrich learning

Implementing cutting-edge technology enriches the learning experiences and prepares students for an increasingly interconnected and diverse world

At the University of Westminster, we have been thematically analysing open comments from student surveys for several years. This academic year marks a change for us as we employ AI to help us in analysing student and staff qualitative feedback.In previous years, we have manually conducted a thematic analysis of about 3,000 students per year, developing themes and uncovering patterns in the open comments response.&nbsp;This was a long and laborious task – especially in comparison with&nbsp;quantitative data, which is often received very rapidly by decision-makers.&nbsp;&nbsp;&nbsp;​Can academics tell the difference between AI-generated and human-authored content?How hard can it be? Testing the dependability of AI detection toolsGo head-to-head with ChatGPT to enhance your students’ personal learningThis year, we have implemented cutting-edge machine learning software that enables us to&nbsp;process open comments responses and provide insights much faster.&nbsp;​We are one of the first UK universities to analyse the results of the National Student Survey (NSS) using AI software (MLY provided by Explorance), leading to better insights into enhancing student experience.We’re still early on in our journey with AI, but here is what we’ve learned so far.Good output relies on good inputAI models available on the market have been trained on relevant data and feedback. If you are seeking to build your own model, or to purchase a model, be mindful that AI systems rely on large amounts of data to learn and perform well&nbsp;– but not all existing data is reliable, valid or representative. For example, student feedback surveys may have low response rates, missing values or &nbsp;leading question wording that affect the accuracy and usefulness of an AI model. Question wording that is leading can also impact the usefulness of an AI model.To make the most of your input, consider the phrasing of your questions and how they might influence the answers. For instance, “Could you tell us one thing you liked and one thing you didn’t like?” Possible responses might be:⦁&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; “Classrooms, quality of teaching”⦁&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; “I enjoyed the teaching but didn’t like the classroom.”Based on the above responses, an AI model may identify themes for the first response (but not likes or dislikes) and sentiments for the second response. You will likely get a more accurate sentiment and theme analysis by ensuring that one question is asked at a time. Changing the wording to “Tell us about your experience” ensures the respondent is providing the sentiment and meaning behind their response (ie, “I enjoyed X, but didn’t like Y”) which the AI can code.Additionally, your survey data may require a process of cleaning to remove jargon or non-responses (such as “N/A”) in your text. This will help you to ensure accuracy and reliability in your analysis. The output can only be as good as the input.Combine your AI analysis with human input and validationWhile AI can provide you with many insights from your students’ comments, it cannot replace the human judgement and expertise needed to interpret and act on them.&nbsp;We decided to purchase AI because we wanted to build on our qualitative analysis capabilities and knowledge. Our institutional research teams’ skill set in thematic analysis, prior knowledge of datasets and experience in inductive and deductive thematic analysis provides our source of human input, interrogation and validation of the AI analysis.&nbsp;We can spot discrepancies between human interpretation and AI interpretation and feed back into the AI model. For example, a human can read into sarcasm in texts whereas AI cannot.&nbsp;The AI model currently only understands what it has already seen (eg, deductive coding) and relies on being taught themes, but humans are capable of inductive coding and can spot new themes.&nbsp;You should always review your AI results with a critical eye. Consult with other stakeholders to make sure that your new insight can be actioned in line with your institution’s key priorities. Be careful to identify which department can action what insight, and whether that might be an academic or non-academic concern – responses about heating, cafeteria options or accommodation would fall into the latter, for example.&nbsp;Also, think about whether these action points are quick wins for a team or important to the institution as a whole. If talks with stakeholders confirm they are neither, then move on to the actions that are.Make the most of AI’s capabilitiesThe AI gives us a greater capacity. We can run all our major surveys (student surveys and colleague surveys) through this system, enabling us to be more responsive, where we can identify in-year issues and react quickly.&nbsp;&nbsp;&nbsp;Bringing in the qualitative data from all our major surveys means we will have the capacity to map different levels of study or different time periods, allowing us to identify patterns and trends. We can segment the data to better understand different student user journeys and develop measures to create a successful experience for all our students. For example, separating out the experiences of those who have been on placements versus those who haven’t, or pre- and post-placement groupings, can show us how they each self-report on their student experience, education and learning gains, and any differences between their responses.We can now share high-level analysis of the open comments almost as quickly as we currently deliver the quantitative counterparts of our surveys.&nbsp;However, without action and accountability, the development of this analysis will not realise its potential. A culture shift in listening&nbsp;to qualitative insight and actioning the insight is required. Stakeholders must buy in to make the most of the use of AI and its outcomes. If this is achieved, we might expect to see a change in future survey results as the impact of these changes is realised.Kirsty Bryant is senior institutional research analyst at the University of Westminster.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

How AI can revolutionise the way we analyse student surveys

Student surveys provide a potential goldmine of data. Kirsty Bryant suggests deploying AI to maximise your insight

Let’s face it: we can embrace artificial intelligence (AI) or fight a losing battle. To do the latter would be to ignore AI’s potential to help lecturers become efficient, to automate administrative tasks, to personalise learning experiences and to enhance the content on offer to students. It would mean denying that AI has become a powerful ally to educators striving to&nbsp;cultivate dynamic and effective learning environments.&nbsp;It doesn’t just stop there, however. To a student, AI can be a useful friend, a revision buddy and a personal tutor.&nbsp;Combine this with a simulation and you have tools that complement each other to offer an immersive experience where students can be inspired to develop curiosity and passion for their subject. This was our experience teaching a Level 5 module on search engine optimisation (SEO) and digital advertising. After searching for the right fit, we signed up to the marketing simulation created by&nbsp;Simbound, giving&nbsp;students a way to turn theoretical knowledge into practical expertise.Marketing simulations: bridging theory and practiceWe used the marketing simulation within our SEO module to enhance our offer of experiential learning. The simulation was based on a Lenovo marketing case study that was adapted to match our bespoke assessment. The students were asked to show how they would increase sales, website traffic and social media engagement. As part of the simulation, they received weekly market-condition reports to aid their decision-making; they designed web pages, landing pages and ads with keyword optimisation for ranking purposes, evaluated and tracked data, used and assigned budgets, performed A/B testing, sent out email marketing campaigns and put together community groups replicating what you would find on social media. The assessment was a summative evaluation of the campaign.Campus webinar: Can universities keep pace with digital transformation?Transition from expert to guide by embracing generative AIStudents see the benefits of AI-generated learning contentThis exposure meant students weren’t just learning from textbooks, but also applying their knowledge and reflecting on their marketing decisions. This helped them to develop the&nbsp;transversal skills they need to leave university with confidence and enter the fiercely competitive job market. This is especially important to widen opportunities for disadvantaged students. The integrated AI assistant supported students with keyword research and writing meta descriptions for the web pages as well as providing guidance on best practice. Students were learning prompt engineering to get the best results.&nbsp;Students on the module concluded that the simulation with built-in AI was “better than just reading about it in a book” because&nbsp;“having evidence that what we were being taught actually works was very reassuring”. Students found the simulation of great benefit as it provided results and the impact of decisions made could be seen. The AI aided students because they were able to focus their planning, revisions and creative decision-making based on the simulation.&nbsp;Incorporating simulations into marketing classes and assessments is a way to create a realistic, practical, engaging and low-stakes environment for active learning.&nbsp;As a result, the ability to analyse data, make informed decisions and navigate the intricacies of marketing landscapes become second nature to students immersed in this educational approach.&nbsp;Personalised learning in actionAI can help with personalising learning experiences for diverse learners. It can enable lecturers to offer adaptive content, bring in interactivity and support real-time feedback opportunities in seminar environments.Marketing simulations provide students with hands-on experiences in crafting and directing marketing strategies. Pair this with an assessment and you have students justifying, recommending, analysing, creating and essentially performing all our beloved Bloom’s taxonomy. It creates an environment conducive to active learning that improves results and increases student engagement and retention.&nbsp;Preparing students for the futureThe combination of simulation and AI helps students hone their strategic thinking as the exercise guides them through the nuances of decision-making. It replicates the complexities of the marketing landscape and instils resilience. These experiences will give our students more than grades to put on their CVs. We are giving them lived experiences they can discuss in interviews.It’s not just about our students, though. The advent of generative AI also requires educators to embrace emerging technologies, not with fear of failure but according to the marketing concept of “test and learn”, to build the skills our students need to learn.&nbsp;By embracing the synergies between AI and marketing simulations, we not only prepare our students for the challenges of the professional world but also invigorate our roles as facilitators of knowledge and architects of transformative educational experiences.&nbsp;Rohim Mohammed is a lecturer of digital marketing at University College Birmingham.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

How do we teach students marketing theory while preparing them for an evolving professional landscape? Rohim Mohammed looks at how artificial intelligence and simulations foster practical, interactive learning 

Transforming marketing lectures with AI and simulations

Visualising data can help students and staff in higher education get useful insights into otherwise opaque data sets –&nbsp;affecting decision-making, improving understanding when teaching and effectively communicating research findings both within and outside academia. Here, I’ll take a tour of data visualisation and offer my tips to help you get started.What is data visualisation?In the simplest terms, data visualisation is&nbsp;the act of conveying information from data as a visual representation. You might be asking yourself: “What does that even mean?” I’m being vague because data visualisation comes in so many different forms and styles, with a lot of jargon attached. To get a feel for different styles, take a look at the visualisations people have made for&nbsp;the Information is Beautiful Awards.&nbsp;Why is data visualisation important?Data visualisation makes it much easier to get the information you need or to tell others about the insights you have found. A classic example from the 1850s is when Florence Nightingale&nbsp;used data visualisation to convey that poor sanitary conditions were causing high mortality in hospitals. The visual provided a clear message that change was needed.&nbsp;How data from digital learning tools can refine teachingDIY learning analytics: using data to improve online teachingCollecting data on our students is the only way forwardAt LSE, we often use&nbsp;data visualisation to explain and explore research findings or illustrate&nbsp;student journeys, for example. From&nbsp;a more statistical standpoint, data visualisation allows you to see trends in your data that would otherwise be invisible. An excellent example of this can be seen in&nbsp;this article by Justin Matejka and George Fitzmaurice, where several datasets with the same statistics, such as averages, result in very different-looking visuals.&nbsp;This all sounds great, but we should still think critically about what we are seeing! Charts can be misleading, demonstrated by this&nbsp;article by Statistics How To. The New York Times has a great resource to help here called&nbsp;What’s Going on in This Graph?, which I recommend taking a look at.&nbsp;How do I make all these exciting graphics?There are four things you need to get started: a data visualisation tool, some data, an idea of what visualisation to make and a can-do attitude. If you’ve not done much data work before,&nbsp;Excel or&nbsp;Google Sheets are good tools to start with, depending on what you have access to – but there are many tools to pick from.Getting yourself some data to play around with is relatively straightforward, as many datasets are available online. I’ve been collating my favourite sources over the years, which you can find on&nbsp;my GitHub. Finally, knowing what visual to make can be overwhelming when there are so many options. I recommend&nbsp;From Data to Viz to ease this process, which provides interactive maps of what visual to use based on your data.&nbsp;It’s important to note that when seeing some excellent data visualisations, like this example from&nbsp;Our World in Data, or&nbsp;static and&nbsp;infographic examples from LSE, you might think: “Wow, those are great, but how easy is it to do that?” I won’t lie, it is hard to get to that level. But that doesn’t mean you can’t create some valuable visuals. Have fun and make good and useful stuff!&nbsp;Let’s talk toolsThere are a lot of different options when it comes to tools. To help, I’ve provided a (non-exhaustive) list and categorised them by non-coding and coding tools. Coding tools require you to write some programming syntax to make a visualisation.Non-codingExcel: most higher education institutions have access to thisGoogle Sheets: you’ll get this if you have a Google account..Tableau: it’s costly but has a free version called&nbsp;Tableau Public.Power BI: currently cheaper than Tableau, and has a&nbsp;free public version. But be warned, Power BI only works on Windows.RAWGraphs:&nbsp;a non-coding visualisation tool without any cost.Datawrapper: a lightweight free version is available, with other paid models.Flourish: similar set-up to Datawrapper.Coding, all of which you can use without costR using&nbsp;ggplot2 or&nbsp;plotly. R is the easiest of the coding tools to use for visualisation.&nbsp;Python has many visualisation libraries. Matplotlib and&nbsp;Seaborn are popular, as is&nbsp;plotly, but I find&nbsp;Lets Plot the best.JavaScript using the&nbsp;D3,&nbsp;React,&nbsp;highcharts (also available in R) or&nbsp;Observable libraries.My advice here is to try out several tools and see what sticks. Trying out programming languages such as R, Python or JavaScript is more of a learning endeavour but worth it if you get serious about data work.&nbsp;Data visualisation and being able to think critically about visualisations you see in the wild is a useful skill – not just in higher education, but in many aspects of life. I hope reading this has given you a taste of the use and potential it offers.&nbsp;But let’s remember that data visualisation isn’t just about charts – it is about design and creativity, which I think makes it more fun! Even so, when getting started it can be helpful to follow some general guidelines and get a bit of theory too. I would recommend Claus Wilke’s book&nbsp;Fundamentals of Data Visualisation.Look for inspiration or a project that is interesting to you because this will give you the drive to keep learning – and hopefully enjoy yourself along the way.Andrew Moles is learning developer for data science tools at the London School of Economics and Political Science.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

The what, why and how of data visualisation for HE

Wondering how to get started with data visualisation? Andrew Moles lays out the basics and offers his advice

The what, why and how of data visualisation for higher education

Almost 30 years ago, Bill Gates, one of the most influential voices in tech, wrote an essay in which he coined the phrase “content is king”. Gates identified that one of the greatest opportunities for success online would be based on the production and distribution of high-quality content that entertains and informs. This predicted a trend that would influence business models for years to come. In his book The Road Ahead, he gave us a glimpse of this future.In recent years, it has become clear that this prediction was pointing towards the use of technology to reach and affect people on a massive scale. In digital marketing, a good content strategy can make a big difference and serve as the best tool to develop user commitment and loyalty (we only need to search online to discover hundreds of available articles, books and videos that discuss the topic in depth to see evidence of this).The pace of evolution in education has accelerated the use of digital technologies – which, in turn, has transformed the way we teach. The digital transformation of education has the potential to fully support high-quality and inclusive education and training for all learners, facilitating a more personalised, flexible and student-centred learning experience.Universities that champion digital education must consider the phrase “content is king” if they want to scale their impact through pedagogical models with technology as a vehicle for doing so. You only need to look as far as Coursera, MasterClass&nbsp;and Skillshare to see examples of tremendous success in this area. Many universities are also now running online courses and degrees. Examples include MITX, edX and ASU Online.Considering that the phrase “content is king” is more than a trending topic, libraries should go beyond traditional marketing strategies to shed light on its content – its articles, eBooks, multimedia assets and others – to attract, acquire and encourage new audiences to consume it.Libraries should redesign their websites to properly showcase their digital collections and reach the right users, convert them into active users and ensure they regularly revisit the content. A good digital marketing strategy for a library should provide a consistent stream of new users and turn first-time visitors into active and loyal ones.Resources on digital transformation in higher educationCampus webinar: Can universities keep pace with digital transformation?Why hybrid learning needs hybrid facultiesAt Monterrey Institute of Technology’s library, we have recognised our role in enhancing digital education’s potential. We also know that creating a digital marketing strategy might be difficult, so we would like to propose some steps below.Get to know your audience: we can’t assume we know everything about the needs and preferences of the academic community; making an effort to understand our users is essential. One way to do this is to create personas&nbsp;(fictional profiles of your target users) and decide on how they might and should interact with your website or services.Develop your unique value proposition: what is important to users can be subjective and difficult to identify. However, you can refer to elements that they value to help you build a solid proposition.Evaluate and measure your user experience: it’s fundamental to draw on and have a clear understanding of user experience mapping, a technique that helps website owners understand what motivates their customers, what they need and what their concerns are to help deliver superior experiences and value for your users.For educators, the type and format of content they choose to introduce into their learning experiences is incredibly important; it can be a catalyst for the growth and development of the student’s journey. The right content adds more context to lesson plans and learning experiences while showcasing various viewpoints and ideas that aim to develop fundamental competencies such as complex and critical thinking.The library’s management of its digital collection is evolving and the amount of available learning content is growing with teaching material curated from within the professional community, as well as from external sources such as open educational resources. The days when students learned only from textbooks and printed materials are gone. The best-structured and enriched digital content collection will energise and engage students, turning the novice learner into an expert and helping to develop the fundamental competencies needed for a lifelong learning journey.José Vladimir Burgos Aguilar is the national director of libraries for the office of the vice rectory for educational innovation and academic regulations at Monterrey Institute of Technology.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus&nbsp;newsletter.

Libraries’ role in digital education: content is still king

University libraries have a huge opportunity to evolve and strengthen their roles. To do this, they must demonstrate the value of their digital content by promoting the use of their digital collections to engage, inspire and enhance learning

The library’s role in digital education: content is still king

Qualitative data has a richness and depth that you don’t always find with numerical data. This is something I quickly discovered on joining the Virginia Tech University Libraries from a background in quantitative social science. Qualitative data can tell a more nuanced tale, and one unlikely to be exhausted in a single researcher’s work.But how do we share qualitative data effectively when traditional data repositories are built for quantitative data? In the ever-expanding landscape of open access research, the Qualitative Data Repository (QDR) is dedicated to advancing qualitative research and promoting transparency and accessibility in the social sciences. This article explores what QDR is and how it works, shedding light on its role in democratising qualitative research.First, I should clarify Virginia Tech’s relationship with QDR.Virginia Tech and QDR are partners in developing infrastructure for open access qualitative data-sharing, funded by a national leadership grant from the Institute of Museum and Library Services.Virginia Tech and QDR are developing training materials for researchers on how to reuse shared data, both for QDR and in partnership with UCLA’s Open Science for Librarians initiative and Library Carpentry.How university researchers work with the Qualitative Data RepositoryEstablished with the goal of fostering transparency and collaboration in the social sciences, the QDR serves as a centralised hub for researchers to deposit, access and explore qualitative data sets. QDR data is accessible to anyone in the world for use in research and education. Some sensitive data do require separate applications for access, but those, too, are open to scholars internationally.&nbsp;QDR has deposits from researchers on all continents except Antarctica.The double-edged sword of open access publishingWhy does open access make publishing more complicated?Eight ways your university can make research culture more openUnlike traditional repositories that primarily focus on quantitative data, QDR has been tailored to the challenges and nuances of qualitative research right from the beginning.&nbsp;Researchers deposit their qualitative data sets with QDR. This contribution involves submitting not only their findings but also original research materials such as interview transcripts, field notes, audio recordings or any other data relevant to the study. QDR has a structured process and curation staff to guide researchers through making deposits and ensure that data adheres to standards.Metadata and documentation play a key role in maximising QDR’s effectiveness, so researchers are required to deposit detailed information about their data sets, including the research design, methodology and contextual information. This enhances the credibility and reproducibility of the research and allows for replication and scaling of the work.QDR operates on the principles of open access, meaning the deposited qualitative data is freely available to the public. But what about sharing sensitive information? Researchers have discretion in setting access restrictions or embargoes based on specific circumstances, such as confidential information and ethical considerations. This way, sensitive information can be protected even as QDR remains open. &nbsp;The QDR platform features robust search and discovery functionalities through keywords, topics and methodologies, which promotes interdisciplinary collaboration. Relatedly, QDR adheres to established standards for qualitative data documentation, such as the Data Documentation Initiative. Similarly, the REFI-QDA Standard&nbsp;allows project transfer between participating qualitative data analysis programs. Projects can be exported from one program and opened in another, where they are converted into the receiving program’s format. Consider that researchers work with their favourite tools and software and may have proprietary licences for these tools. Adhering to established standards means that not only are data sets on the platform easily located and understood, they are also compatible with and transferable through other repositories, software and tools.Recognising the varied needs and experiences of researchers, QDR provides training and support through webinars and documentation resources to help researchers navigate the deposit process, understand best practices and harness the full potential of the platform. When users need assistance, real-time help is available from QDR experts.A real-world example: advantages of sharing data on QDRIn 2023, I worked with Martina Svyantek, a Virginia Tech graduate student who was examining three universities’ policies from a critical disabilities point of view. Policies can be notoriously difficult to track down, but she was successful in doing just that, helping to understand how disabled students, staff and faculty were supported, monitored and controlled in university settings. She collected and analysed the data for her dissertation.When I approached her about sharing her data through QDR, she initially demurred but ultimately agreed. Her data has already been downloaded hundreds of times; it provides a model that can be replicated and extrapolated. For example, it could be expanded to see if her findings apply to other colleges, such as community colleges or liberal arts schools. Instead of sitting on her hard drive or being lost outright, the data preserves and shares her work as a durable resource for herself and others to use and cite.The QDR stands at the forefront of the open access movement, providing a secure, transparent and accessible space for qualitative data. As one of only a few widely accepted qualitative repositories, QDR plays a pivotal role in advancing the principles of openness, collaboration and reproducibility in qualitative research, contributing to the rich tapestry of qualitative knowledge.Nathaniel D. Porter is social science data consultant and data education coordinator at Virginia Tech University Libraries and an affiliated research assistant professor in the department of sociology at Virginia Tech.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

Traditional open access repositories are designed for sharing quantitative data. But what about qualitative data – non-numeric information and concepts such as case studies or interview transcripts? That’s where the Qualitative Data Repository comes in

Sharing qualitative research through open access

There has been an unprecedented increase in the use of Artificial Intelligence (AI) software since OpenAI’s launch of ChatGPT in November 2022. As of December 2023, ChatGPT has 180 million regular users. Students and researchers can take advantage of ChatGPT’s properties to craft literature that mimics human-authored content. But it’s such a recent innovation that there is limited research exploring the practicability of using ChatGPT within research writing. 

There is limited understanding of whether individuals can successfully distinguish between AI-generated and human-authored text, and if they regard AI-generated text as higher quality. A recent experimental study we conducted aimed to investigate the following questions:


	Can psychology students and researchers distinguish between AI-generated and human-authored journal abstracts?
	Does the level of experience the researcher has influence their ability to distinguish between AI-generated and human-authored content?
	Do psychology students and researchers evaluate the textual features of AI abstracts differently to the way they evaluate human abstracts?


As AI evolves and more organisations take advantage of models such as ChatGPT, it will become more important than ever to explore their ethical and practical use so we can understand what role they can play in education and research.


	Transition from expert to guide by embracing generative AI
	Don’t just chat(GPT): turn on critical interrogation
	Artificial intelligence and academic integrity: striking a balance


Structure of the study

Our sample consisted of 56 participants from accredited psychology institutes, of which 27 were undergraduates, 15 were postgraduate students and 14 were researchers who had completed postgraduate study. Each participant was given a survey that consisted of 10 abstracts from varying fields across the top psychology journals. Five of these abstracts were human-authored and five were generated by ChatGPT. The input or “prompt”&nbsp;we provided ChatGPT was consistent across all generated abstracts. We asked ChatGPT to generate an abstract based on the information we provided such as title, name of journal, the methodology and the results.

Within the survey, we asked participants to evaluate the given abstract as AI-generated or human-authored. Additionally, we asked participants to rate, on a five-point scale, the textual features of the given abstract such as fluency, readability and clarity. This evaluation matrix was referred to as the Text Evaluation Scale (TES) and was scored out of 25.

What did we find?

We discovered that participants were generally quite bad at discerning between AI-generated and human-authored content. With a score just above chance, participants had a 52 per cent success rate at identifying the given abstract correctly. Interestingly, participants with the highest level of researcher experience were the least successful at this task. Undergraduates and ongoing postgraduates had similar scores to each other, and both performed better than completed postgraduates at a statistically significant level. 

Even more interestingly, participants evaluated AI-generated abstracts to have significantly better textual quality than human-authored abstracts. Our findings, though significant, should be interpreted with caution - our sample size was relatively small, which means it’s difficult to generalise these findings to the rest of the general population. Furthermore, the TES is a newly created scale that has not undergone sufficient psychometric testing to render it a credible measure of linguistic text. Further research is needed to consolidate our findings.

What does it all mean?

Our study highlights that AI text generated by ChatGPT has the ability to effectively mimic human-authored text, presenting a range of implications. First of all, it establishes that researchers can incorporate AI-generated content within their work to make the research process more efficient. Researchers can focus on more critical aspects of research while delegating written content to AI. 

In fact, as AI-generated content is evaluated to be of higher textual quality, researchers can improve their literary compositions using AI. But the inability to discern AI-generated text shows that academic breaches can happen without detection. This makes the attribution of authorship unclear – can human authors claim authorship of content generated by AI?

The innovation of ChatGPT means non-English-speaking researchers could use AI to compose English text that is high in quality, allowing researchers to publish in international journals and foster further collaboration and development within science. However, some institutions could restrict use of such technology among researchers and this would create inequity within the research community.

Pathway to the future

Our study highlights the need for AI literacy to be incorporated into academic education. Students and academics alike must be made aware of the properties of AI and its ethical use through academic programmes or specialised training in university settings. They can take advantage of software like ChatGPT to generate well-worded work but must tread carefully if their institutions regulate such AI use. The use of ChatGPT sparks questions about authenticity and authorship that academics must consider thoroughly.

There is significant potential for models such as ChatGPT to become effective research tools. Academics can utilise our research to build on findings and add a new perspective to this growing and important area of study. Our research focused on the field of psychology, but the use of AI to craft literary pieces has practical implications in a range of fields. Academics could explore ChatGPT’s creative writing aptitude and compare it to human-written content. The implications of ChatGPT are vast, and time will tell how effectively we can use this ground-breaking technology.

Omar Siddique is a postgraduate student at the University of Adelaide.

If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus&nbsp;newsletter.

Can you tell the difference between AI and human content?

A recent study asked students and academics to distinguish between scientific abstracts generated by ChatGPT and those written by humans. Omar Siddique analyses the results

Can academics tell the difference between AI-generated and human-authored content?

Campus webinar: How will HE keep up with digitalisation?

Three Australian digital education experts from the Campus+ network explore the benefits and risks of AI and VR in teaching and learning, and gauge higher education’s pace of digital transformation

Campus webinar: Can universities keep pace with digital transformation?

THE podcast: microcredentials are knocking. Will HE answer?

Tips from 10 years of Moocs at the University of Edinburgh and a call to arms to embrace technology for greater equity and financial sustainability

THE podcast: microcredentials are knocking. Will higher education answer?

Remember when we used to say “It was better back in my day”? That saying is now obsolete thanks to generative AI, which has rendered comparisons between the past and present almost irrelevant. This AI-driven magic has not only helped us conquer hindsight bias but, more importantly, has rekindled our inner students.

My first encounter with generative AI (gAI) left me excited and apprehensive. The prospect of whipping up an essay in five minutes seemed like a dream. But, as with any revolutionary tool, questions started to swirl in my mind. How would this affect my role as an educator and researcher? What about my students?


	Embrace AI tools to improve student writing
	ChatGPT and the future of university assessment
	Enhancing manuscript clarity and ethical credibility with ChatGPT


We’re often told that the greatest teachers are those who are students themselves and yet, in our capacity as educators, making time between teaching, research and our personal lives to be a student is difficult. But the entrance of gAI shifted the balance. We had no option but to adapt and learn, and in doing so, we truly became students once again.

At first, I struggled with the gAI interface, feeling overwhelmed and confused. I bonded with colleagues who shared this feeling&nbsp;– we were no longer the experts, but instead had to find a way to best use gAI to our advantage while teaching our students to do the same. This experience provided a stark reminder that as much as I could try to sympathise with my students in theory, their struggles were far more profound. It allowed me to connect with them on a deeper, more personal level, with a greater sense of compassion and empathy. 

As faculty, we tend to draw from our own experiences, attempting to motivate students with tales of our past successes. However, I’ve come to realise that while this might instil hope for the future, it does little for them in the moment when they’re feeling overwhelmed. 

Hindsight bias, with its tendency to look back at the past through rose-tinted glasses, finds no place in the classroom. Our experiences certainly shape our perspectives, but putting them on a pedestal is neither fair nor helpful.

That said, we’re all too aware of how easy it is to succumb to hindsight bias, especially when interacting with a generation different from our own. Grappling with gAI forced me to confront and dismantle this bias. Compared with my own undergraduate days, today’s world has undergone a radical transformation. We are thrust to the present, facing this new normal that we are all part of.

This transformation has enhanced our dynamic with students in three ways:


	By acknowledging our own fallibility, we build trust.&nbsp;When uncertainty looms, as it always will in new territory, there is power in simply saying, “I don’t have the answer. What are your thoughts?” It’s simultaneously frightening and exhilarating to be reminded we have so much more to learn and discover. In admitting this, we demonstrate vulnerability and give students an opportunity to see us in earnest – and build trust. 
	&nbsp;In sharing our wildest ideas, we encourage our students to do the same.&nbsp;That bold new notion you were toying with? Bring it to the table for discussion. “Have you guys tried this with gAI? How well did it work for you? Oh, there’s another platform that’s better? Cool, thank you.”&nbsp;As we share our ideas with students, we create a space for learning that they feel comfortable enough in to share theirs.
	&nbsp;With a more open rapport, we can guide students to greater clarity of their own strengths.&nbsp;As we are increasingly open with our students, I have found that they now approach us more frequently with their innovative discoveries.&nbsp;“Prof, I experimented with this technique. What are your thoughts?”&nbsp;“Hmm, it brings up some good points. But in your original, you set up a cohesive narrative that would draw your audience in. How about combining the two?” 


Engaging in these dialogues, I realised we’ve transitioned into experienced guides, instead of mere subject matter experts. We are in a position to offer perspectives on students’ progress and convey that gAI isn’t a replacement, but a tool that enhances their skills. We can assist them in navigating its intricacies and help them advance their abilities.

With the advent of gAI, gone are the days of saying “When I was your age” or “Back in the day”. We’re squarely in the present, embracing the journey of learning and acquiring new skills. We’re here, more in tune with our students than ever before, thinking, “I’ve felt that way too. Let’s navigate this together.” So let’s embrace the now, our shared learning experience and our evolving skill set, fostering unity in understanding.

Kyrin Liong is an assistant professor at the Singapore Institute of Technology.

If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

Transition from expert to guide by embracing generative AI

In the process of learning how to use generative AI, Kyrin Liong rekindled her inner student 

What works best for improving your productivity? Since the pandemic, the working world has adopted a more flexible perspective on motivation, assisted by increasing reliance on technology. I&nbsp;believe academics must go on their own personal journey to find the techniques that work for them.

We juggle multiple responsibilities, such as teaching, research and administrative duties, and each requires different skill sets and approaches to improving productivity. I’ve worked to prevent burnout by creating space and taking the time to re-evaluate myself as a human being.

Employ productivity tools

My productivity improved when I adopted effective habits, optimised my daily tasks and calculated my time management efficiently. These all require multitasking, so set clear goals for each of your duties and use relevant productivity tools. Organisation is as necessary as personal motivation for increasing productivity.


	Cameras on: how to overcome a key challenge in digital courses
	Eight ways to improve your digital well-being
	Resource collection: unplug from academia


Use project management software, such as Smartsheet, and record meetings and events on Google Meet to increase your presence in both physical and digital spaces. This provides you with a panoramic construction of your workflow, making it easier to prioritise tasks. By doing this, I&nbsp;can break down tasks into smaller jobs to make them more manageable and keep track of everything I’m responsible for, such as the deadlines of student assignments and the progress of my research submissions.

Manage your time

Time management is crucial for balancing your work schedule. Assigning specific time slots for each of your responsibilities will help keep you focused. It’s important to identify distractions that could affect productivity – turn off notifications on your phone or block certain websites. When I&nbsp;can mark the progress of my tasks in project management software at the end of the day, I&nbsp;feel both relieved and motivated to work harder.

Take it easy

Plan gentle activities such as simply looking out the window or being in nature to bring joy to your life. Focus first on your own mental and physical health by prioritising self-care, which in turn will help you cope with your daily tasks. Setting a rigid routine for tasks can lead to a loss of productivity, because in academia, many processes and research activities are necessary. It’s important to be honest with yourself. Come up with your own plan and find solutions to motivate yourself. Taking a day off, even half a day, or meditating can help.

When we can organise our resources and measure the outcome, productivity improves. Finding the ideal blend between my academic goals, administrative duties and research responsibilities has helped improve my efficiency, while also allowing me to make time for myself and my well-being. Modern technology can be indispensable once you have defined your priorities and desired outcomes. By harnessing its powers, I&nbsp;have&nbsp;not only improved my motivation but enabled myself to engage with and support my students better, facilitating an improved personal experience within the academic world.

Ayşe Hyusein is a lecturer and researcher in business administration at Near East University.

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Use technology to boost productivity and beat burnout

Ayşe Hyusein advises using a range of techniques and tools for more effective time management

Presentations on the use of artificial intelligence in higher education often start with promising titles that convey positive messages about AI’s potential for learning. Discussions may even feature catchy phrases such as “deep learning”, which reflect a hopeful view of AI’s impact and the trend towards embracing this technology in the HE industry. These presentations set goals and provide examples of how AI can foster an innovative, often creative learning environment, one we might describe as “next gen”, borrowing a term from the world of video games.

However, during open discussions, the focus often shifts to ways to combat, restrict or dismiss AI’s role. This appears as a self-defence reflex, even if the initial scope of the discussion is geared towards embracing AI. But what are we afraid of? 

Remember the days when students had no choice but to search for the nearest library? You had to work to its operating hours, travel there, request hard copies of textbooks or scientific papers, sift through materials. Then, after all those tasks, you could finally concentrate on the most critical learning content. The world then met the internet, and this process evolved – now scholars can use research-oriented search engines, which significantly optimise our time. Imagine if your educational institution insisted on wasting your time organising these searches – believing that the process taught you organisational and perseverance skills – instead of allowing you to focus on the content you desired to enhance your critical thinking.


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In fact, it is the subtle opposition to the bots such as ChatGPT, Bard AI and Microsoft Copilot that is the potential threat to students’ critical-thinking ability. Recognising that the development of critical thinking usually requires effort, persistence and a willingness to face challenges, educators worry that students might perceive AI as offering a trouble-free journey, that they believe AI technology will entirely replace the need for intellectual hard work. The direct implication of this mindset is that students might not only fail to learn adequately but, more significantly, may lack a comprehensive understanding of the tasks at hand. 

So the focus turns to adjusting assessments to limit the usefulness of AI (time-constrained quizzes, oral presentations replacing essays, policies suggesting restriction levels at the discretion of programme leads). But I advocate a temporary pause on critical thinking in favour of using AI to explore the prerequisites for critical thinking. I propose my own next-gen version of learning: critical interrogation.

The journey to knowledge, to gain intellectual skills and wisdom, often starts with an inquisitive attitude and a persistent desire to understand. This eagerness to know through questioning is evident in children, and it’s something we should strive to preserve into adulthood. Encouraging curiosity and the search for deeper understanding can be seen as a prerequisite for critical thinking; it is a more complex skill that encompasses problem-solving, synthesised information analysis, reasoned judgement and evidence-based decision-making.

When a student encounters a complex problem, we teach them to break it down, analyse solutions and solve the problem. In light of this reasoning, it becomes apparent that we should explore how to use AI to foster inquisitiveness and generate interrogative conversations. Such conversations can be seen as standalone assessments where the quality of the interaction is evaluated. This approach exposes learners to multiple learning objectives, including asking “why?”, seeking clarifications, breaking down complex theories, rephrasing complex text for better understanding and verifying the reliability of information. 

Students are exposed to the positive use of AI: learning how to optimise prompts and create interrogative dialogues tied to curiosity. Also, the interrogation exercise will frequently lead students to discover the limitations of AI, including hallucinations and incorrect claims. As a result, they become aware of the areas in which AI falls short.

Because the bot generally offers the option to save conversations, it can easily be used either as a drafting tool to create a new independent piece of work to be submitted as an assessment or as a standalone assessment where the prerequisites of critical thinking are assessed. 

Pause critical thinking, enter critical interrogation!

Giuseppe Cimadoro is academic integrity lead and a senior lecturer in strength and conditioning science in the Faculty of Sport, Technology and Health Sciences at St Mary’s University Twickenham.

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Don’t just chat(GPT): turn on critical interrogation

Critical thinking is often seen as the antidote to generative AI. But what if educators took it one step further back and aimed to encourage students’ curiosity? Giuseppe Cimadoro explains

The Global Shared Learning Classroom, an initiative in which two universities from separate countries solve a real-world challenge&nbsp;together using digital communication tools,&nbsp;is a catalyst for connecting students and professors from different parts of the world in a collaborative virtual environment. Using the collaborative online international learning (COIL) methodology, it’s proven to be an effective vehicle for breaking both geographical and cultural barriers.

The GSL Classroom methodology is based on COIL, which was conceived by Jon Rubin at the State University of New York (SUNY). His approach emphasises the collaborative design of activities and the integration of COIL methodology, allowing students from all over the world to collaborate virtually. The programme extends for a minimum of four weeks, ensuring a deep immersion in course content. Participation in multicultural teams is essential, enhancing students’ intercultural skills and significantly enriching their educational experience. This holistic approach aims to provide substantive knowledge and promote global understanding and collaboration in an international educational environment.


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Our experience in the GSL Classroom highlights the importance of fostering active participation, designing challenging activities and facilitating teacher collaboration for effective global learning. Adaptability to cultural diversity emerged as a key factor, helping us create an enriching experience.&nbsp;

Here are our four tips for success with the GSL Classroom specifically:


	Encourage active participation:&nbsp;The key to GSL Classroom success is fostering active student participation. This goes beyond simply completing tasks –&nbsp;it involves engaging in meaningful discussions, sharing personal experiences and contributing to an enriching global dialogue. We use a metaverse, a 3D communication platform,&nbsp;to do this.
	Design challenging and relevant activities:&nbsp;Activities that address real-world challenges, especially those related to the UN’s&nbsp;Sustainable Development Goals, generate greater engagement. These activities stimulate creativity and motivate students to apply innovative solutions to global problems. We focus on finding solutions to generate clean and affordable energy (SDG7).
	Facilitate teacher collaboration:&nbsp;Collaboration among teachers from different parts of the world&nbsp;not only benefits students but&nbsp;enriches the teaching experience. Establishing spaces to exchange best practices, methodologies and resources strengthens the global community of educators. We had prior planning and organisation sessions via Zoom. The metaverse also offers a place to meet for academic activities.
	Adapt to cultural diversity:&nbsp;Cultural diversity in the GSL Classroom is a strength but also requires special sensitivity. Creating an environment that celebrates cultural differences and fosters mutual understanding is essential for the programme’s success. An icebreaker activity is an excellent way to foster cultural diversity and promote mutual understanding in the GSL classroom.


Here we offer five further recommendations, based on our success with GSL Classroom, for implementing similar programmes in your institution.


	Plan activities and meetings with international partners. Define goals, assign responsibilities&nbsp;and anticipate challenges in planning activities and meetings with international partners. Detailed documentation of agreements ensures shared understanding and effective follow-up. This practice optimises collaboration time, strengthens relationships&nbsp;and contributes to the ongoing success of the GSL programme. Cloud-based word processing applications such as&nbsp;Google Docs make this task easier.
	Integrate technology meaningfully. Use technological tools not only as support but&nbsp;as key facilitators for global collaboration. Interactive platforms, video conferences&nbsp;and online collaboration tools are fundamental for the GSL Classroom. For example, video conferencing systems such as Zoom, collaboration platforms like Google Workspace and real-time collaboration tools like Padlet and WhatsApp have all proved invaluable.
	Establish clear and measurable goals. From the outset, defining clear and measurable goals helps guide the programme. These goals should focus on internationalisation, promoting global education and effective collaboration between students and professors.
	Encourage continuous reflection and evaluation. Structured reflection at the end of the programme is vital. Encourage participants to evaluate not only the results but also the process to ensure continuous improvement and adaptation to changing needs.
	Explore international partnerships. Seeking partnerships with other universities and educational centres internationally expands collaboration possibilities. These partnerships can enrich diversity and perspectives within the virtual classroom. This strategic approach not only positions the institution for greater visibility but also provides a valuable differentiator, highlighting its commitment to global collaboration and educational excellence.


The GSL Classroom at Monterrey Institute of Technology&nbsp;is not only a model of educational internationalisation but a rich source of learning. Active participation, careful design of activities and effective global collaboration are key for success. These tips and recommendations provide a guide for colleagues and professors at other universities looking to implement similar initiatives, creating a global fabric of learning.

Jorge Alvarez is an associate professor at Monterrey Institute of Technology.

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Tips for mastering the Global Shared Learning Classroom

The Global Shared Learning Classroom can help extend internationalism beyond traditional exchange programmes. Jorge Alvarez offers advice

The Covid pandemic highlighted the extent of digital exclusion, particularly within higher education. Nevertheless, digital education continues to augment (even replace) traditional face-to-face delivery, with educators making increasing use of digital technology. This shift can exclude many learners because of a lack of digital literacy, limited access to technology or disruptive home environments. The cost-of-living crisis has further affected digital inclusion because most university programmes require (at least basic) digital skills and access to digital technology. Meanwhile, employers expect graduates to demonstrate core digital skills.In a time where digital transformation is redefining the boundaries of knowledge and learning, it is imperative to create an inclusive digital environment in higher education for students, staff and their local communities.Resources for lifelong learningWhy hybrid learning needs hybrid facultiesThe dark side of AI’s promised efficienciesAt Staffordshire University, we have adopted the Digital Inclusion Manifesto, employing the accompanying framework to drive forward our inclusive practice. The framework is based on three core principles: accessibility; equality, diversity and inclusion; and empowerment. We benchmarked ourselves against the framework and identified areas where we could implement measurable interventions, leading to key initiatives.Build staff confidenceWe updated our organisational development strategy and created new persona-themed digital skills training packages for staff. We created a digital skills group, with leaders from across the university, to implement, evaluate and refine the staff digital skills framework, ensuring that a customised journey is mapped out for each staff member to develop their digital skills and confidence.This has already improved our onboarding process and is helping boost staff confidence in their digital capability. We’ve seen academic staff more effectively use technology-enhanced learning, which is raising teaching standards and improving consistency across our digital offerings to students.Empower studentsWe have worked with Jisc&nbsp;to deploy its digital diagnostic tool that allows users to self-report confidence in digital skills and applications. They then receive a personalised report identifying areas of high and low confidence. In our implementation, the user is automatically directed to the most relevant learning resources, thereby hyper-personalising digital skills development. We also embedded the diagnostic into the welcome week activities for incoming students and used it to create learning packages for staff to use during induction and early in the semester. Two-thirds of students who completed it reported a positive improvement in their digital skills, making comments along the lines of: “I’ve gained a better understanding of my digital skills and knowledge gaps and use this information to identify areas where I can improve.” Students who engaged with the digital diagnostic and associated learning resources were able to gain points in our student enrichment programme. These points can be stacked to achieve awards and certificates that can be added to CVs, improving employability prospects.Alongside the digital diagnostic programme, we have run digital-activation days for students. This starts in welcome week and continues throughout the year, allowing us to deliver face-to-face, interactive and engaging digital skills training for students that ranges from AI to MS Office. These sessions&nbsp;have improved the digital competencies of our students and boosted their digital confidence, and helped them understand the importance and key role of digital skills in career and employability development.We have also launched a free micro-credit course that potential students can access to familiarise themselves with &nbsp;the university’s digital systems and develop core digital skills. We have partnered with local sixth forms and colleges to monitor and evaluate the impact these modules have on students’ knowledge, skills and transition success.Meanwhile, the university library team has worked with colleagues across the university to create two innovative tools to help improve digital inclusion. The virtual escape room is an immersive tool designed to invigorate students’ interest in scholarly enquiry and foster critical-thinking and problem-solving skills. We have also deployed Minecraft Education to create an alternative front-end client to the library’s website, improving both the appeal and accessibility of library resources.Policies and practicesWe have taken steps to explicitly incorporate digital inclusion in our teaching and learning resources across the university. We have developed and implemented a mandate for all courses and modules to establish a standardised structure and minimum level of accessibility in digital resources.Alongside this, we have created a new role, the student digital engagement officer, responsible for engaging with students on their digital skills development. Acting as a bridge between our teaching innovation and learning enhancement hub, library, EDI initiatives, digital services and the students’ union, the officer develops key programmes and work streams aimed at enhancing digital provision for students as well as being the focal point for help and support. The officer was able to engage more than 200 students in initial welcome week sessions and has delivered over 30 training events this academic year.Access to digital technologyStaffordshire University, along with 11 other organisations, launched the Discover Digital project with support from the government’s Community Renewal Fund. The project is designed to boost digital inclusion in our local communities by identifying, understanding and breaking down barriers to digital access. Through this project, we provided laptops to students who demonstrated need, and digital support and training to some of the most digitally excluded members of our community. We also issued grants for laptops and devices for those facing financial barriers to digital access. The project opened a pop-up shop in the city centre and delivered bespoke digital training and support in community venues across Stoke-on-Trent to reach people who were most digitally excluded.In conclusion, being involved in the development of the Digital Inclusion Manifesto and using the framework has allowed us to identify our strengths and weaknesses, create and implement a coherent programme of interventions to explicitly embed digital inclusion and create road maps for future digital interventions. We will continue to implement the framework more widely and deeply across the institution.Raheel Nawaz is pro vice-chancellor (digital transformation) at Staffordshire University.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the&nbsp;Campus&nbsp;newsletter.

A road map for advancing digital inclusion

A multi-departmental and collaborative approach to digital skills training has helped tackle digital exclusion at Staffordshire University. Raheel Nawaz explains the key initiatives 

A road map for advancing digital inclusion for your students, staff and community

Generative artificial intelligence (G-AI) has taken the world by storm over the past year. While many other institutions have focused solely on implementing G-AI tools in education, at the Hong Kong Polytechnic University (PolyU), we have also adopted ChatGPT as a gamification tool, motivating students and enhancing their domain knowledge, critical thinking and reasoning skills. Our ChatGPT v&nbsp;Professor Competition is a fun way for students to engage in a subject and for teachers to gain a deeper understanding of their students’ learning.

Encourage critical thinking and logical reasoning

We developed the ChatGPT v&nbsp;Professor Competition to examine how G-AI could be adopted in education and improve students’ critical thinking and reasoning skills. The professor and ChatGPT took turns providing their responses to commonly asked questions on a subject, and students voted for the best ones.


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By comparing the human and the machine responses, students gained a deeper understanding of the chatbot’s capabilities and limitations, enhancing their critical thinking and reasoning skills, while also evaluating their own knowledge of the subject.

Overall, ChatGPT was seen to provide a fairly comprehensive summary in its responses, which effectively supplemented the human response on several occasions. However, its responses can be dated because it relies on the sources and volume of data it was trained on. The latest version of ChatGPT can access real-time data, but this does not mean that it can incorporate the data into its learning.

While ChatGPT is an excellent source for student learning, providing vast amounts of data and timely information, students should also conduct additional research when fact-finding.

Enhance students’ motivation and communication skills

The ChatGPT v&nbsp;Professor Competition can also serve as a way to interact with overseas institutions and to collect different views on the same set of questions. We carried out the ChatGPT v&nbsp;Professor Competition as part of our Global Classroom activities, where students from PolyU and a foreign institution attended an online class together. The benefits of this activity exceeded our expectations, as feedback from the students involved shed light on the cultural differences between them. For example, usually introverted Asian students were more willing to share their thoughts during this activity, realising that the professor’s responses to the questions might be incomplete, obsolete or just wrong. ChatGPT could fill in the gaps, providing comparatively accurate and up-to-date information.

Improve the quality of teaching

By using the ChatGPT v&nbsp;Professor Competition and discussing the results, educators can gain a better understanding of students’ progress, ability to articulate concepts and any misconceptions they may have. They can use it to refine or revise teaching methods, content and pedagogies based on this information – and respond more effectively to the same questions in the future.

Teachers as well as students can learn more about the strengths and weaknesses of ChatGPT and G-AI tools in general. The ChatGPT v&nbsp;Professor Competition is discipline-independent and, as such, can be used for any subject and at various levels. It can also be combined with other initiatives – for example, we have organised the same competition in two Global Classrooms, enabling us to analyse the cultural factors influencing responses among students from various countries.

ChatGPT has proved to be an effective gamification tool to stimulate students’ disciplinary learning, metacognition development and critical thinking. We believe the ChatGPT v&nbsp;Professor Competition initiative provides a win-win-win for students, teachers and institutions, strengthening student motivation and personal learning and improving the quality of teaching.

Eric Tsui is a senior project fellow at the Hong Kong Polytechnic University.

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Go head-to-head with ChatGPT to enhance personal learning

Pitting yourself against artificial intelligence can enhance your students’ motivation and critical thinking. Eric Tsui shows how

Go head-to-head with ChatGPT to enhance your students’ personal learning

Small universities have few staff, tight budgets and little flexibility. Delivering big change is hard for institutions at any scale – but how can you make your resources stretch when you have very little to play with? Here’s what Birmingham Newman’s Times Higher Education Awards-nominated library team learned while overhauling their digital services. 

Believe in yourself

Birmingham Newman University, which began training teachers in 1968, serves around 2,500 students mainly studying social sciences and humanities. The library has a staff of 20 people, many working part-time.


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It took the Covid-19 pandemic to show us that we could deliver just as effective, timely and ambitious a response as many larger and better resourced institutions. That sense of confidence is irresistible. At the time, a colleague said: “We can never again say ‘We can’t do that’.” Believe in what is possible and go into your project with a glass-half-full approach. 

If you are going to change, change

Many systems are going through generational changes as legacy systems are phased out and more integrated approaches become the new normal. But changing a system you have used for 25 years can be daunting.

The pandemic was a great opportunity to try new approaches and for our students to get used to change.

With a well-planned and well-coordinated approach, you can do more than you imagine. We tendered for, procured and implemented a new library management system (LMS). But we also took the opportunity to review and revise our website and transition our social media strategy to focus on Instagram and TikTok. Implementing these changes at the same time felt like an overall fresh approach and gave us a real sense of buzz. 

Good planning is key

We consulted thoroughly with staff to understand what functionality was essential and what was only necessary for the way we were operating.

Using our LMS contract termination date as the end point, we worked out ambitious but realistic timings for all stages, including securing institutional approval, tendering, assessing suppliers, implementation and rollout. We were relentless in sticking to milestones and worked hard to anticipate most problems in our initial planning. Integrating the different work streams helped to use our staff’s time most effectively.

Look for what you want, not what you have

Instead of trying to build on what you’re doing currently, look at what the new systems can offer. 

For us, this meant taking a fresh look at all our policies and workflows to see what we would like to do, rather than what we had done in the past. We’d streamlined our processes over many years to make the best of our system’s functionality. But new-generation systems do things differently. To move forward, some treasured workarounds have to go.

Remember your friends

One of the major considerations in a project like this is systems integration. We needed to focus on who our internal partners were and what impact our project would have on other areas of the university. At the time, implementing the LMS was one of the largest projects for both the IT department and the library. Building mutual communication and understanding the pressures each department was under were key to delivering well. 

Lean on the sector

The HE sector is rich in cooperation and consortia support. Advice from your mission group, support from a professional network and assistance from a sector body can be invaluable. Make the most of your connections when you are moving into new territory or coming from a low asset base.

We were assisted by the insights of other libraries and particularly by the reciprocal network of purchasing consortia that gave us access to the framework agreements of APUC, the Scottish procurement group. Coupled with advice from our own procurement group, SUPC, access to APUC’s LMS framework made the tendering process manageable for us.

Be prepared for disappointment

However well prepared you are, there will be things that don’t go as you hoped.&nbsp;Your carefully honed words in the specification turn out to be more ambiguous than you thought; a feature in development is dropped by the supplier; a key staff member leaves and you have to revise roles and responsibilities.

These things happen. The longer the project, the more likely they are to occur.&nbsp;Don’t become disheartened that your project won’t deliver all you aspired to. Make the best decisions you can in the circumstances. It might turn out better than your original plan.

They think it’s all over

The project doesn’t end when it goes live. This is the beginning of a new journey of innovation and service enhancement. Continue to use the wider resources of the sector. Get involved in user groups and ask account managers to introduce you to colleagues elsewhere who are working on projects of interest. Regionally, we have become one of the founding members of the Mercian Collaboration’s Library Systems Group. These offer opportunities to develop skills and broaden the pool of knowledge, helping to make the best use of our investment.

Chris Porter is director of library and learning services at Birmingham Newman University. 

Birmingham Newman University is shortlisted for Outstanding Library Team at the Times Higher Education Awards 2023&nbsp;#THEAwards. A full list of shortlisted candidates can be&nbsp;found here.

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How small universities can make big digital changes

Even tiny institutions can punch above their weight when implementing changes. Chris Porter shares the lessons learned from overhauling library digital services at Birmingham Newman University

Students enrol on to equine courses because they have a passion for horses. This doesn’t always mean they know how to ride a horse. Students often come with no prior riding experience or with “bad riding habits” that have been formed through unawareness and repetition over time.

Learning to ride horses takes time and is steeped in traditional methods of teaching. Instructors often stick to these traditional approaches because they are seen to be the main way students learn. However, these methods are not effective for all participants, and those with challenges in learning often struggle to understand and build their skills. 

In response, we have developed an innovative way of using technology that challenges these historic and limited learning environments. Though we’re working in a niche field, how we have used technology to create a more inclusive learning environment for riders, regardless of their background and experience, holds lessons for all educators.

Tracking cameras

The Pixio Robot Camera System has auto-follow capabilities for moving objects that track the rider as they go around the arena. It allows riders to review their performance and see what the instructor sees (which they commonly do not feel in themselves). The rider can also see the impact this is having on the horse and correct their fault(s) for the welfare of the animal. A series of recordings can be used to track progress over time and relevant exercises set for the rider to work on independently.&nbsp; 

Saddle pressure mat

To give students a more in-depth understanding of the impact the rider’s position has on the horse, we use the Estride Harmony saddle pressure mat. It is placed on the horse’s back and under the saddle. It downloads data from 920 pressure points to an app and displays in colour where the most pressure is on areas of the back.&nbsp; 

This pressure mat can be used for teaching saddle fitting and for riding pressure. When the rider perhaps sits crooked in the saddle, the pressure mat will highlight the uneven weight on the horse’s back, which if uncorrected will lead to pain for the horse and uneven muscle development. It can also have long-term impacts on the rider’s strength and muscle development, and potentially lead to problems in the future.

Biometric jackets and symmetry markers 

We also incorporate the Centaur Biomechanics mesh training jackets and symmetry markers in our coaching sessions. These provide highlighted lines desired for the classical position to help promote and develop a correct riding position. With the robot camera system and saddle pressure mat, this addition makes it even clearer for the rider to evaluate their own position when riding and when jumping.&nbsp; 

Rein-pressure sensors

Another area that significantly affects the horse’s performance is the connection between the rider’s hands and the bit in the horse’s mouth. The amount of connection through the rein will vary among horses but a common fault in riders is stiff and poorly positioned hands, elbows and shoulders. A rein-pressure sensor system helps the rider to understand more about the amount of pressure on the left and right side of the horse. This very useful device has a box that sits on top of the horse’s head and has two lights that indicate the left and right rein. The lights are on a colour-coded system and change with the pressure applied by the rider on the horse’s mouth. The feedback to the rider is instant and they are in control of adjusting themselves.

For nervous riders it is a place to focus their attention and can be a subconscious distraction.&nbsp; 



  
      
  
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Rein-pressure sensors on a horse give nervous riders confidence



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Position monitors

We use Nagga, a small jocularly named device that is calibrated to buzz or beep if the rider enters the parameters of incorrect positioning. It can be fitted to the rider’s hat to aid those riders who look down when riding, fitted to the rider’s wrist for those who drop the hand too low or too high and on the ankle for riders who raise the heels when riding. In any of these positions, the device gives instant feedback to the rider and promotes self-correction. All these positional faults make the rider stiff and unbalanced, with effects on the way they and their horse will perform.&nbsp; 

Getting the basics of classical seat riding right is imperative for success in equine science, and this technology makes that achievement available to more students at a reasonable cost to the university. These devices can be used on their own or with a combination depending on the needs of the individual, their learning styles or their experience. And in supporting riders we’re improving horses’ welfare.

Our experience shows the value of using technology to break down the barriers of a traditional learning approach so that it is more inclusive. These technologies could also be adopted for the evaluation of a range of practical learning situations, including for the development of positioning in sports or physical performance evaluations. It opens the options for diversity of learning, and with this comes inclusivity. 

Jennifer Fitzpatrick is an equine lecturer at Scotland’s Rural College

Scotland’s Rural College (SRUC) has been shortlisted for Technological or Digital Innovation of the Year at the&nbsp;Times Higher Education&nbsp;Awards 2023 #THEAwards. See the full list of shortlisted candidates. Winners will be announced at a ceremony on 7 December in Liverpool.

Academics and university leaders from across the UK and Ireland will come together that week at&nbsp;THE&nbsp;Campus Live UK&amp;IE&nbsp;to talk about institutional strategies, teaching and learning, the student experience and more.&nbsp;Join us&nbsp;for this two-day event in Liverpool.

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Use technology to make equine education inclusive

A group of educators at Scotland’s Rural College have reached more diverse students by using wearable technology to update traditional methods of teaching horse riding

Get off your high horse: using technology to make equine courses more inclusive

Public access to generative artificial intelligence (GAI) made a dramatic leap forward with the release of Open AI’s ChatGPT in November 2022. Students rapidly adopted the technology to assist them with their written assignments and universities reacted in a variety of ways, including trying to find ways to detect if students had used AI. A common plagiarism-detection tool used by universities is Turnitin, which, from April 2023, includes an AI detection feature. This led a team from the University of Adelaide’s Unit for Digital Learning and Society to test this AI-detection tool, and a few others, to see how easy it is for students to fool the system.&nbsp;

Testing Turnitin’s AI detection

To create a simulated sample of student work, ChatGPT was asked to write a critique of the 2004 Yi-Mou Zhang movie House of Flying Daggers. Turnitin’s detection tool successfully spotted the AI-generated text, returning a result of 100 per cent AI-generated content. ChatGPT was asked to rewrite the movie critique to make it “more humanlike”. The resulting output still did not trick the Turnitin detection tool. However, after ChatGPT was asked to rewrite the critique in the style of a 14-year-old school student – and then again, a fourth time, with “more random words” – things changed. With ChatGPT’s conclusion to this final attempt at the movie critique – “This flick is the bomb-diggity. You won’t want to miss it, bro!” – Turnitin was unable to detect the AI content, returning a 0 per cent AI content in the final test.


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A second test used a sample 500-word essay composed by ChatGPT on the topic of academic integrity. The essay was then modified using strategies to replicate how a student might try to trick the AI detection: sentences were paraphrased and restructured, and a few spelling mistakes were deliberately included. The Turnitin AI-detection result went from 100 per cent for the ChatGPT-generated sample to 39 per cent for the modified essay. &nbsp;

To further replicate how some students are known to be using AI, a third test was conducted in which ChatGPT was asked to improve paragraphs that were known to be originally written by a human to make them sound “more academic”. The first paragraph was extracted from one of the researchers’ own previous journal publications, and the second stage used two paragraphs taken from a transcription of one of their oral presentations at an academic conference. In both cases, Turnitin was unable to detect AI-generated content in these submissions.&nbsp;

Testing online AI detection tools&nbsp;

The research team then tested some of the freely available online AI-detection tools using the same tests and samples, to wildly varying results. Some tools proved reasonably reliable. However, in other cases, the AI-detection tools returned a lower score for the AI-generated or modified samples than the original human-written samples. The most reliable tool was found to be Copyleaks, with a result of 85.2 per cent probability for AI content for the movie critique written like a 14-year-old school student, and 73.1 per cent probability for AI content for the essay even after it had been altered by a human. &nbsp;



  
      
  
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Copyleaks detected an 85.2 per cent probability for AI content for a movie critique of House of Flying Daggers written in the style of a 14-year-old school student



&nbsp;

However, some tools returned peculiar results, including a false positive from&nbsp;Content at Scale,&nbsp;which returned a 99.9 per cent AI content score on the ChatGPT-assisted human-generated paragraph. &nbsp;&nbsp;

It is important to note that these tests were conducted in July and August 2023. As the tools and ChatGPT evolve, these figures are likely to change. None of the AI-detection tools proved extremely reliable and even the best of them could be easily tricked. AI-detection tools are still in their infancy and will learn to improve just as AI content generators will learn. A Cornell University report, Can AI-Generated Text be Reliably Detected?, reminds us: “As language models become more sophisticated and better at emulating human text, the performance of even the best-possible detector decreases.” It is unknown how this battle between the two technologies will unfold.

It is recommended that educators test AI’s ability to complete their own assessment tasks and consider ways students might be using ChatGPT or other AI generators. Testing AI generators using a range of AI-detection tools will give educators a wealth of useful information about the design of their assessments. They will be able to ascertain how easy each assessment task is for students to use generative AI to assist in producing work. It will also give valuable information regarding which types of assessments are most resilient to AI.

Furthermore, by doing this, educators will familiarise themselves with the red flags for AI-generated content within their own assessment portfolios. The results of this study suggest that Copyleaks would be a valid online tool to begin this process. But the real takeaway is that we should assume students will be able to break any AI-detection tools, regardless of their sophistication. The goal for educators is to now design assessments that encourage and test learning rigorously in an AI world. This is a challenging sector-wide task – but one that is long overdue.

Daniel Lee is postdoctoral researcher in the&nbsp;Unit for Digital Learning and Society&nbsp;and Edward Palmer is associate professor, both in the School of Education at the University of Adelaide.

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How hard can it be? Testing AI detection tools

Students are using artificial intelligence to write essays and other assessment tasks, but can they fool the AI detection tools? Daniel Lee and Edward Palmer put a few to the test

How hard can it be? Testing the dependability of AI detection tools

Deep-learning technologies and generative AI models have gone from technical niche to hot topic. Language tools such as ChatGPT and Meta’s LLaMA, alongside image generators like DALL-E and Midjourney, are among the most talked-about technologies being used by our students. However, their formal integration into UK creative arts programmes remains limited.

The latest advances in AI should be contextualised within broader trends of access and opportunities, relating to a range of social, technological and demographic factors. A significant shift is under way in the very fabric of future learning. It is this fast-approaching future that higher education must take part in actively. One approach is to work with the technology ourselves and recognise the need to engage with the next generation of students before they enter our institutions.


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Educators should engage with AI not just as users but as influencers, understanding its functions and the intentions behind AI tools, and aligning them with educational goals. This involves a continuous process of social experimentation and adjustment. The adaptation of creative arts programmes through AI involves two critical principles:


	Widening the public conversation: Universities must enable a broader, more inclusive array of constituents to access AI and have a stake in the public conversation. They can create opportunities for more bottom-up approaches, enabling skills and giving voice to the next generation’s thoughts, understanding, trust and experience of AI while informing their own practices.
	Learning the machine, learning to learn: Universities need to commit to young people’s creative and professional futures, as these will be shaped by the new technologies. They should plan swiftly and deliver the physical and virtual infrastructures required for the high-level, agile meta-learning that young people need.


As an initial response to this landscape, we have developed AI-informed approaches that we refer to as “learning the machine”. We speak with students directly about the experience with AI tools and applications, feeding this back into a more engaged process of learning. The aim is to rapidly develop and test new ways of teaching and learning in creative arts programmes and related creative domains. Learning the machine is not mere semantics but optimistically calls for a deeper critical and practical engagement with new technologies. Learning through making enables a more critical, productive approach. 

Demis Hassabis, CEO of Google DeepMind, advises young people, especially those aged 16 to 25, on the importance of meta-skills like “learning to learn” for future careers. He highlights the need for agility in integrating new tools with existing skills. Our method involves cultivating these meta-skills by using AI tools practically in classrooms and community workshops, including a project with Tate Britain (see below). This is particularly aimed at reaching young individuals who are either still in school or are not typically engaged in higher education but who are equally part of the generation who will be most impacted by, and “native” to, a burgeoning AI ecosystem.

Learning the machine means first accessing new technologies. It means a free-form approach to social and experiential learning; it means opportunities for robust and inclusive debate, based on findings having used the new software and tools. Learning the machine means observing the benefits and limitations of these tools first-hand. It allows for reflection on one’s own interests, needs and desires. Overall, testing and establishing new social conditions or situations of bringing people together to learn when working with young people enables new insights for rethinking the art school of the future. 



  
      
  
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AI workshop In the Mix ≈ London with participants from Element at Taylor Digital Studio as part of the University of Southampton-run Electronic Life programme of public events for Late at Tate Britain. Photograph: courtesy of Kingsley Davis.



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We have developed this approach with Tate in the context of Tate Britain’s Taylor Digital Studio. Working alongside coders, facilitators and researchers, for example, through co-curated experimentation, creative practice and discussion, we witness the younger generation as active agents, navigating an emerging AI realm with respect to their everyday lives. Through this work we have been supporting Tate’s future strategy for capability building and equally questioning the conditions for change in the university setting. 

Universities need to ensure better social spaces for learning – spaces that are agile technologically (not locked down by legacy systems or static hardware). These spaces should be flexible, socially inviting and therefore more conducive to being/working together, allowing young people to test ideas, solve problems and advance an understanding of AI tools’ impact on their daily lives.&nbsp; 

Institutional responses and policies regarding generative AI are lacking or too hasty. Within the university setting, the approach tends to be top down, focusing on mitigating risk and warning against the threats to academic integrity. What is missing is the more bottom-up approach described here, which seeks to work collaboratively and provide hands-on experience for young people, even before they reach university. 



  
      
  
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Public participants at In the Mix ≈ London at the Late at Tate Britain event in October 2023 at the Taylor Digital Studio. Photograph: courtesy of Kingsley Davis



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The availability and embedding of AI tools are only increasing. Adobe’s Creative Cloud, for example, viewed as an industry standard for art and design students, directly incorporates advanced AI plugins. These kinds of tools are already shaping the future workplace. Early access to industry applications alongside a wider array of developmental tools is not only empowering for young individuals and community groups, it is critical.

Learning the machine is not just advisable, it is foundational.

Ed D’Souza is professor of critical practice and Sunil Manghani is professor of theory, practice and critique at Winchester School of Art at the University of Southampton. Both are members of the Alan Turing Institute’s AI &amp; Arts interest group.

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How tech is shaping the future of creative education

Educators should engage with artificial intelligence and other technologies not just as users but as influencers, write Ed D’Souza and Sunil Manghani 

Learning the machine: shaping the future of creative education

Virtual reality can make introductions more fun and expose students to new technologies. Here, Elvira Luna offers her tips for introducing it into COIL classes 

Using VR to break the ice in the classroom 

We are talking again about ChatGPT’s potential for student dishonesty. A report in Times Higher Education alerts us to ChatGPT’s capacity to respond to images, meaning that students could, in theory, simply take a picture of an exam paper containing images or diagrams, and ChatGPT would be able to answer the questions. Earlier this year, a BBC story emerged documenting uses of ChatGPT by students at Cardiff University, and reporting that a student’s ChatGPT-enhanced essay had been awarded a first – the highest grade that student had obtained.Something else is worth paying attention to here. We are still talking about spotting quite obvious wrongdoing. There is no intellectual difficulty in working out what has happened in these or similar situations: a student has pretended to do work that they had not in fact done. The challenge is in detecting the dishonesty – but our definitions of dishonesty, cheating and misconduct remain intact.&nbsp;So far, at least.But we can’t yet know what kinds of cheating ChatGPT might make possible in the future. We can’t yet know if we have a full taxonomy of ChatGPT-enhanced mischief. Perhaps all our definitions will remain intact or perhaps something new is rumbling down the tracks towards us.ChatGPT and generative AI: 25 applications to support student engagementHarness human and artificial intelligence to improve classroom debatesCollaborating with artificial intelligence? Use your metacognitive skillsRight now, we might say, any act of academic misconduct falls along a kind of continuum, in part because it is all susceptible to the same types of detection (an alert lecturer noticing a change in writing style, plagiarism-detection software). Along this continuum fall the student who has a chaotic note-taking style and ends up being unable to distinguish between their own words and those of a source; the student who grabs a single paragraph from an internet source and doesn’t attribute it; and the student who buys an essay from an online essay mill.These would all be clear examples of students (accidentally or deliberately) passing off someone else’s ideas as their own. But using text from ChatGPT is perhaps not quite the same thing – because ChatGPT is interactive, generative and creative. An internet source exists before and outside a decision to steal from it and is unchanged by the act of stealing from it. But ChatGPT only produces text in response to a prompt; one must interact with it.So far, we might think that ChatGPT-produced text is clearly on the continuum I was outlining above – that is, a student including ChatGPT text in (for example) an essay would be passing off another’s words or ideas as their own. But the generative, interactive capacities of ChatGPT take us in another direction. A large language model (LLM) such as ChatGPT producing text in response to a prompt can surprise us – and we might very well want it to.It’s for this reason that Mike Sharples, in a recent THE piece, says that he intends to use ChatGPT to “augment” his thinking. He means, I think, that its generative capabilities might prompt him to create presentations or papers in ways that he would not have done previously. He wants to be surprised: the prompt you give ChatGPT can always generate text you were not expecting.What this means is that it’s hard to bind or limit an LLM. We might say to students that they may use it up to a point or that certain limited uses of it are legitimate on certain courses – but its responses could still exceed such limits. It could still surprise us.Let’s try a thought experiment.As tools such as spelling and grammar checkers are legitimate, we decide that we can allow students to use ChatGPT to go slightly further and improve the surface-level features of a text. A student might then prompt ChatGPT to check the coherence of a text or to make sure that paragraphs have topic sentences.Here is where the student might be surprised.The resulting reorganisation of text is such that a new thought, or a new line of argument, crystallises in it. If we accept that thinking and language are related (and I think we must), then this is a possibility with an appropriately sophisticated LLM. This student might now respond to ChatGPT’s text as though they are themselves being prompted and refine the text further. And then they might involve ChatGPT once more, producing a new text, and so on. We are now talking about a student and a machine co-writing and responding to one another, creating a complex, multi-stranded text braided together from the work of both student and machine.This would be something different from splicing “chunks” of text written by ChatGPT into an essay.It might very well be misconduct – but I don’t think it falls on the continuum of misconduct I described above. It is the generative capabilities of LLMs, their capacity to surprise us, that would make such a collaboration with ChatGPT possible. We will need to think carefully about how misconduct is defined when collaboration like this – which may very well be an authentic, meaningful learning process – is possible.I don’t think we are yet at a point where this thought experiment can become reality. But then, this time last year the conundrums we now face with LLMs were not reality, either.So, how might we, as educators, respond to these new circumstances? I can think of a couple of ways.One is that we might need to write modules in which we tell students that they can use ChatGPT to their hearts’ content – it will not be classed as misconduct, but they need to document what they are doing. Such an approach would allow us to get an understanding of what students are doing and how they are incorporating LLMs into their own work habits. We could then start refining our definitions of misconduct in the light of what we find out.Related to this, we might need to prepare for the idea that the use of LLMs could be encouraged in foundation or first year and that students would be expected to decrease their reliance on them as their expertise increased. We could expect text produced by ChatGPT in assignments to be appropriately labelled as such. This would echo some of the things we know about plagiarism, which can be usefully seen as a “stage” immature scholars need to pass through.Our starting points, in other words, should be understanding how students might benefit from using LLMs at different points in their degrees. From there, we might consider what uses are legitimate and what might be “too much”.Tom Muir is associate professor of English for academic purposes at OsloMet – Oslo Metropolitan University, Norway.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus&nbsp;newsletter.

We can’t yet know if we have a full taxonomy of ChatGPT-enhanced mischief, or whether certain uses should be classed as mischief at all, writes Tom Muir

Will ChatGPT change our definitions of cheating?

Ever found yourself repeatedly rephrasing a question to a generative AI model, such as ChatGPT, despite continuing unsatisfactory answers? Perhaps you’re subconsciously using the same mental shortcuts you rely on in human interactions? AI complements our thinking but isn’t the same as human intelligence; we can’t just rely on it and switch on our cognitive autopilot. In a prologue to this article, we explained why metacognition should be especially important when collaborating with AI. Read on to explore how to strengthen your metacognitive skills and avoid falling into such cognitive traps.

Metacognition involves the ability to step outside of our current cognitive focus and think about our thinking. It involves the use of knowledge (about one’s own skills and the task one is working on) and mental regulation (monitoring what one is doing, whether these strategies are working and then using this information to adjust what one does going forwards).


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Metacognition in practice

In ordinary terms, metacognition is just thinking about our thinking. However, there are four steps involved when metacognition is used to improve learning and performance. In the context of collaborating with AI, they involve:


	Awareness: Recognising when one’s strengths, weaknesses and cognitive biases are influencing the way in which we communicate with and respond to AI output.
	Planning: Leveraging this self-awareness to optimally divide tasks between oneself and the AI to meet objectives.
	Monitoring: While working, monitoring both your progress and the AI’s progress towards objectives.
	Evaluation: After the task, reviewing what went well and what did not in order to refine your approach to future collaboration with AI.


Improving awareness

Most people engage in metacognitive processes, but collaborating with AI necessitates a sharpened awareness of one’s strengths and vulnerabilities. First, we should think about where the AI can enhance the task’s performance while also identifying when our own human intelligence is essential to achieve optimal task performance.

It also helps to know something about the heuristics or mental shortcuts that our brains use and how these influence our decisions. When applied inappropriately, these heuristics turn into biases because they oversimplify complex situations, leading to errors in decision-making. Examples of such biases include:

Anchoring bias: Giving more weight to the first piece of information we receive. Users might allow the response from the AI to limit the range of information they consider, thus overlooking an optimal solution.

Confirmation bias: Seeking information that confirms our beliefs without looking for alternative information. AI tools draw from a massive pool of data. If we allow our bias to direct the AI (“find me evidence that xxxx”), it will probably find information to confirm our beliefs – even if there is far more information to support an alternative belief. 

Halo effect: Using one positive attribute or experience to infer that another will also be positive. Remember that, unlike humans, AI can perform extremely well on tasks they have been trained for but very poorly on others. 

Sunk-cost fallacy: Persisting with an existing strategy because of the time, effort or money invested. Since the AI is trained on specific data, it might not be able to provide the exact solution you are pursuing (for example, a particular way of structuring your code). Instead of trying a new tool, you think: “I’ve put in so much time, I should keep going.”

Sometimes, we might mix in biases like not trusting automated choices (“algorithm aversion”) or thinking we know more than we do (“overconfidence bias”). These can make us miss out on valuable AI insights. But here is the good news: there are strategies that we can use to help us minimise the impact of these biases.

Planning, monitoring and evaluation

One of the most effective ways we can foster metacognitive thinking is by using prompts. While working with AI, we should be prompting ourselves to plan, monitor and evaluate, asking questions such as:

Planning: What is it that I am trying to achieve on this task? What types of errors do I need to look out for when using this AI tool on this type of task?

Monitoring: Am I making progress towards our objectives? What biases might be influencing my judgement?

Evaluation: Could I have made better use of the AI on the task? What should I do differently next time?

Designing computer applications that show prompts during tasks is efficient, and they can automatically display reminders to monitor progress as well. But just keeping track isn’t enough; knowing how sure you are about your knowledge of strengths and weaknesses is equally important. Sometimes we think we know more than we do, and sometimes less. This can make it hard to track how we’re really doing. Studies show that tailored planning (P), monitoring (M) and control (C) strategies (see figure below) improve learning and performance on complex tasks. Also, feedback that compares how sure you felt after doing something to how you really did helps track progress accurately.


  
      
  
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Figure: Metacognitive strategy instructions for AI collaboration

Mindfulness

While feedback can help us to monitor progress, understanding the inner workings of our brain can also help us control our thinking. Our brain works in two modes. In one mode, we are cruising on autopilot; in the other, we are more focused and aware. When teaming up with AI, it is tempting to stay in cruise mode and lean on the AI. But the key to effective collaboration is the ability to complement one another through awareness of each other’s strengths and weaknesses. A technique that can train our brain to switch from autopilot mode to a state of awareness is mindfulness. Mindfulness helps us move from just reacting to being aware of our thoughts, thereby promoting metacognition. At its core, mindfulness involves focusing on a single object or thought and, if your mind wanders, slowly redirecting it back.

To conclude, let’s wrap up with a few simple habits to adopt when we begin interacting with generative AI. 

Start by making a simple plan about what you and the AI are good at – nothing too intricate. While doing the task, pause often to notice what cognitive biases you’ve been relying upon and whether there might be an alternative approach. Finally, try practising mindfulness daily. It is all about consistent practice, even if it is just for a small duration every day. The AI revolution is here, but we need to embrace it mindfully.

Claire Mason is a principal research scientist and leads the technology and work team at the Commonwealth Scientific and Industrial Research Organisation (CSIRO).

Sidra is a postdoctoral fellow at the CSIRO, specialising in collaborative intelligence. Her work centres on optimising collaboration between artificial and human intelligence with a focus on unlocking the full potential of the human mind.

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To follow their previous piece on why metacognition is crucial for collaborating with Generative AI, Sidra and Claire Mason reveal how to boost those specific metacognitive skills

How to strengthen your metacognitive skills to collaborate effectively with AI 

To help academics and university staff get the most out of generative&nbsp;AI to support their work, we are presenting 100 applications of ChatGPT over a five-part series. We have shared an&nbsp;introduction to&nbsp;prompting generative&nbsp;AI for teaching and learning, followed by 25&nbsp;prompts relating to teaching and assessment, then 25&nbsp;to support administrative tasks and a further 25&nbsp;focused on boosting student engagement. We finish by focusing on how to use the AI tool to support research and the scholarship of teaching and learning.

1. Research writing assistance: Please assist me in authoring a research article on early assessment in higher education intended for an academic audience in Australia. Tell me what I&nbsp;should do and where I&nbsp;should start, and provide a brief outline of each stage and a time frame. Now develop an eight-week schedule to complete.&nbsp;

2. Research questions for research: Can you develop 10 research questions regarding the considerations needed for an LMS review? Now use the 10 research questions above and order them from the hardest to the easiest to complete, with what research methods and design each would require, and if ethics approval needs to be obtained, as well as data analysis techniques.&nbsp;

3. Research trend analysis: I want to conduct research trend analysis in the field of artificial intelligence. Can you help me identify emerging trends and potential areas of exploration? Include dates of importance, fields of thought, areas of contention and debate, and include prominent authors.&nbsp;

4. Assistance with lab, government, NGO or NFP reports: Develop a 500-word report aimed at the Northern Territory government to increase community engagement at Charles Darwin University around its new campus in the city. Write in report style. Cite other universities and surrounding communities. Act as a senior leadership member of the university.&nbsp;

5. Assistance with grant writing: Assist in writing a grant proposal for HEPPP [Higher Education Participation and Partnerships Program] funding regarding developing a subject-agnostic cross-cultural competency online module that integrates into Blackboard. Develop an abstract, the importance, and the increasing need for greater competency. Develop for the Northern Territory context. Write as an academic from Charles Darwin University. Outline why Dr&nbsp;XYZ is competent to do the grant. Use this previous HEPPP grant for structure and context. Use these HEPPP criteria.&nbsp;


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	Campus webinar: Artificial intelligence and academic integrity
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6. Generate project ideas: Using the Horizon report, develop 10 research project ideas for my postgraduate education students regarding the 10 most important needs in the next 10 years –&nbsp;for example, artificial intelligence. Weigh each based on urgency, importance and governmental priorities in Australia.&nbsp;Develop an assessment where students need to use generative AI in their development. Now critique the ideas generated with other overlooked aspects within strategy documents.&nbsp;

7. Provide support for PhD students: Can you provide feedback for this PhD student’s abstract and proposal? Highlight their strengths and weaknesses and use this rubric [copy and paste the rubric] as a reference. Outline how they could improve their writing. Do not focus on content. Act as a PhD supervisor.&nbsp;Now focus on their content with several follow-up questions for each paragraph so they can follow up. Put it in the context of the example above.

8. Generate research designs and hypothesis and null hypothesis: Can you generate hypotheses for scientific research on whether students who seek help are not always the ones who need support? The theme is “academic support and help-seeking behaviours”. Include a control group of students not seeking help. Include an experimental group of students who do receive support. Develop a research methodology that will suit and a research method to deploy and assess.&nbsp;Also, include a null hypothesis.&nbsp;

9. Assistance with qualitative data analysis: Help me analyse research data from a spreadsheet of 10 participants who were interviewed about using Microsoft Flip in university teaching. Extract two significant quotes per participant, summarise their use cases and identify common themes. Present similar results together and consider all actions from the perspective of a researcher at an Australian university.&nbsp;

10. Provide help for a presentation: Can you provide real-time feedback and dot points to include in my presentation on academic integrity in the age of artificial intelligence? Act as a senior researcher presenting on this subject. On one slide, place the implications, and on the next future directions. The last slide should include recommendations. Include a reference list and evidence. Include the ethical implications. Offer 10 dot points on factors overlooked or underestimated in academic integrity and artificial intelligence.

11. Provide real-time critique: Can you provide a list of 10 main critiques to professional development workshops for academics? Now develop workable solutions for these. Write for an academic audience.&nbsp;

12. Generate strategies for scholarship: Can you generate a draft or literature review summary of indigenising the curriculum across Australia for an upcoming research paper? Target it for Commonwealth countries. Develop an extensive reference list of literature. The target is an academic audience. Act as a research academic. Develop a list of 10 strategies to indigenise university teaching and learning in Australia.&nbsp;

13. Create interactive databases: Act as a teacher or programmer and develop an interactive database using this prompt for research purposes. It is for a research project focused on analysing the population growth of different cities over the past decade. I&nbsp;would like to include the city name, population in 2010, population in 2020, and the percentage change in population. Display in tabular format.

14. Methodology, sample size, theoretical framework and questionnaire suggestions: Can you help find the most suitable methodology, sample size, theoretical framework and questionnaire suggestions for research on the experiences of students in student as partners (SaP) programmes at an Australian university. Act as a researcher.

15. Organising, chairing or posing conference questions: Develop prompts for this conference on [insert details] and great follow-up questions regarding this issue. Act as the chair of the session on [insert details]. Use their title to inform your response.&nbsp;

16. Quantitative statistical support and analysis: I’m conducting a study to determine if there’s a relationship between a person’s age and their income. I&nbsp;have collected data from 100 participants, including their age and income. I’m not sure which statistical test to use and how to interpret the results. I&nbsp;have the data in an Excel spreadsheet. How can I&nbsp;calculate this using Excel?&nbsp;

17. Improve student evaluations: Provide alternative methods and strategies to increase student evaluations of my teaching. Use this qualitative feedback from last year’s teaching from students and make a list of recommendations that I&nbsp;can apply for this year. Now turn this into a research plan on using evaluations to increase student satisfaction.

18. Ethics guidelines, consent forms and information sheets: Can you develop an example consent form and information sheet on my research that aims to interview 10 students for one hour online via Zoom. Write a list of things I&nbsp;need to consider before, during and after the interview.&nbsp;

19. Identify biases: Identify potential biases in research outputs or the following information [copy and paste the relevant information]. Act as an academic at an Australian university. Be critical.

20. Research recommendations and gap identification: Act as a research associate at an Australian university. List the seminal papers in this area, open education resources (OER). I’m interested in identifying research gaps in the field of OER. Can you help me find areas where further research is needed in the Australian context?

21. Language translation and interpreting: Interpret the following paragraph into English from Chinese [specify your own language choice]. Outline the key ideas or arguments and counter-arguments of what is being said. Write suggestions for further exploration of those ideas and arguments. Now turn this back into simplified Chinese.&nbsp;(Note: Microsoft PowerPoint has a live translator for your PowerPoint that provides real-time captioning in several languages as well as English.)

22. Automated experimental design: I’m planning to conduct a series of experiments to study the effects of different factors on plant growth. Can you help me automate the experiment design process to maximise efficiency and effectiveness? How can I&nbsp;implement this in my own research?

23. Generate ideas relating to PhD supervision: As an academic, generate ideas for in-class activities for a PhD research session on the pedagogy of supervision. Offer suggestions on activities relating to the research design and its benefits and drawbacks. Draw me a session plan to deliver these. Offer 20 suggestions for effective supervision based on research.&nbsp;

24. Automated citation check and improvement tool: Make a list of any 20 citations with APA errors in the reference list; then use the automatic citation generation function to automate the process of generating citations and bibliographies, ensuring accuracy and consistency in academic writing. Now highlight 10 of my previous errors and ways to improve my citations.

25. Organising a conference: Help me organise a conference on student as partners to be hosted at Darwin waterfront. Provide a detailed description of everything that needs to be organised, from room bookings, to inviting panellists, to after-event feedback evaluations and gifts for the panellists. Include a call for papers, a conference theme, and several sub-themes. Place this into a table.&nbsp;

Please note that prompts and various applications of AI output should adhere to the fundamental values of the International Centre for Academic Integrity: honesty, trust, fairness, respect, responsibility and courage.

Seb Dianati is an academic lead for digital learning initiatives, and Suman Laudari is a digital learning designer, both at Charles Darwin University.

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In the fifth and final part of their series looking at 100 ways to use ChatGPT in higher education, Seb Dianati and Suman Laudari share 25 prompts for the AI tool to support research

ChatGPT and generative AI: 25 applications to support research

Generative artificial intelligence (AI) has stirred great debate and discussion across higher education. Opinion pieces, blogs and social media feeds are full of questions with no clear answers, yet educators are having to grapple with AI concerns in their classes. AI-generated text is widely accessible and constantly evolving, so educators must figure out how to adapt and what to adopt.&nbsp;

AI technology has the potential to revolutionise and elevate the educational journey for students by introducing personalised learning experiences tailored to individual needs. It presents avenues for enhancing accessibility in education. Nevertheless, it is imperative to address potential ethical issues that accompany its integration, which raise questions including:&nbsp;what is the potential impact on academic integrity? Will technology encourage cheating? To what extent should AI-generated data or tools be allowed for university teaching and learning? How do we adapt our teaching in a world where the majority of people can turn to technology for a response?

These issues are not novel. Plagiarism, cheating and academic integrity have been at the heart of ethical discussion for years. Unfortunately, AI tools have become associated with academic dishonesty. 


	Resource collection:&nbsp;AI transformers like ChatGPT are here, so what next?
	ChatGPT and AI writers: a threat to student agency and free will?
	Campus webinar: Artificial intelligence and academic integrity


Technological use has been increasingly embraced by teachers over the past 10 years as more information and instruction is delivered online, with mixed success. Such tools have enhanced the potential reach of educators and increased flexibility in instructional delivery but there remain challenges associated with varied computer access and skills, increased isolation and self-direction. AI-generated text is the latest shift in higher education, prompting us to reflect on what learning means now and in the future. 

AI enhances the availability of information and can handle complex tasks, analyse large volumes of data and make decisions with minimal human involvement. So, when applied to teaching and learning, the key question is: when does the use of AI or technology become a violation of the learning process? 

The use of AI and other technological tools does not inherently hinder learning; the critical factor is how to use the technology while maintaining academic integrity. Information generated by AI can be inaccurate or misleading, meaning it will fabricate research, fake a source, not attribute authors for their work, and obscure students’ comprehension of subject matter. This can undermine the educational process. 

Despite the limitations, it is important to explore the potential of technology to enrich students’ productivity and learning experience. Instead of taking a punitive stance, educators should model responsible use of new technology and show how AI can support learning, demonstrating potential structures, approaches or perspectives. 

In academic writing, syntax, grammar use and maintaining originality and clarity are paramount. However, at times, selecting the appropriate words and structure can be a hurdle. This is where tools like Quillbot and Grammarly step in. These help students to enhance their written work by improving the expression of language and correcting spelling, grammatical errors, punctuation blunders or awkward phrasing.

Tools like ChatGPT-3 and GPT-4 can also assist with effective writing. Students can be asked to assess the tool’s response to a major question or concern in their discipline. This allows them to employ critical thinking skills to determine the quality and depth of the AI response. 

In this regard, an effective approach involves having students engage with generative AI to elucidate theories linked to real-life occurrences. For example, students could be asked to use generative AI to examine the Syrian migrant crisis using a specific migration theory. By assessing the practical application of a theoretical concept and related AI response, students are encouraged to analyse the theory from different angles and build comprehension of that subject area.

AI can and should be a complement to student learning, not a substitute. The ability to critically evaluate and assess text is an essential academic skill – one that will become increasingly valuable as students encounter more AI-generated content. 

Future efforts should focus on defining the “appropriate use” of AI tools. Students should be educated not only on the capabilities but also on the limits of AI. In this way, universities will nurture a generation of learners who are adept at harnessing technology but also equipped with critical skills for discernment and effective application.

As AI becomes ubiquitous, universities must equip students with the necessary skills and understanding to use these tools responsibly. Universities that embrace AI thoughtfully to ensure that human interaction, critical thinking and the value of in-depth education are preserved will likely thrive in this new educational landscape. It is important to strike a balance, whereby AI enhances and supports rather than violates the learning process. 

Georgina Chami is a lecturer in the Institute of International Relations at the University of the West Indies.

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A look at how universities can encourage the ethical and transparent use of artificial intelligence tools to support learning while guarding against misconduct

Artificial intelligence and academic integrity: striking a balance

First, let’s be clear: we don’t hate buttons. But we do have an issue with how some components of web pages, including those click-inducing buttons, are implemented across digital interactions. Let me explain…

Our busy content design team is spread thinly across multiple projects, services and products. So, when members of our university community make requests for web pages, often with little explanation about what they are trying to achieve and who their users are, this can be incredibly challenging for our team. One critical pain point is how we interpret stakeholders’ suggestions and decide whether to implement them.


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We also talk a lot about consistency of experience, content standards and what would help speed up the design process. This article reflects some of these conversations, conclusions and decisions we’ve come to as a team, as well as what we’re doing about them.


Linking content design to university strategic goals


To maintain consistency across our website and other digital services, we work from&nbsp;a design system, which is effectively a library of guidance, examples and design patterns that focus on key digital interactions. It allows us to move at pace and provide effective digital experiences that support the&nbsp;university’s strategy and its goals, such as equality, diversity and inclusivity.

When we talk about content design in the context of design systems, we are not necessarily talking about style guides. We are referring to how we create repeatable design patterns,&nbsp;backed by research,&nbsp;and agree upon their correct use as a team. Having a clear understanding of the solution to a problem allows it to be reapplied when the same problem comes up somewhere else – and at scale.

But there is more to it.


It’s all about context


Turning a content design eye to your own design system helps keep products consistent and scalable. It also helps keep designers sane. They have to understand this collection of components, patterns and tools and, more importantly, the right contexts in which to use (or not use) them.

Without this, you can easily lose the benefits that those components were designed to provide, such as greater accessibility for users who rely on assistive technology to browse websites.

Consider, for example, a common website component: the contact panel. We looked at research groups and the need for the contact panel, in terms of discoverability and consistency of experience. The design system is an especially valuable tool. In our content management system, there’s a lot of flexibility with the component itself. This flexibility allows us to present many different combinations of contact information consistently, but also increases the risk of the component being misused. Here’s an example from the Coherent X-ray Science group:


  
      
  
    Image
                



          

  


&nbsp;

Checking in with the design system helped remind our content designers of best practice. The design system removes the need to rely on other members of the team for this information and so also helps them to progress work more quickly.


So what about buttons?


We have nothing against buttons. We’re only using them as an example of a content design decision when building a product or service. 

When a component has context, you can make the right decision by default. For instance, our design system contains information about&nbsp;buttons&nbsp;and what they communicate. It gives explanations and showcases our button types. It’s flexible, but it can’t always tell you when to include a button or not.

When you add the context and content choice to the system, the designer knows the right button, for example, for each content type.

At this point, we probably want to stop thinking about buttons in isolation and turn our attention more to a sense of journeys and the next steps a potential user group may choose to take, if at all. And that’s what we’re hoping to move towards.

For example, our new&nbsp;Postgraduate Research topic pages&nbsp;include Drupal fields where primary buttons can be applied using simple HTML code. These are present in the design system but used sparingly by content designers despite the technical flexibility to use them more liberally.

Here’s our design system page for this product, including&nbsp;the guidance&nbsp;for “How to apply”, which uses primary buttons. When cross-referenced with&nbsp;the component guidance, it’s easy to understand that the primary buttons are for important tasks.

Journeys can be broken if there is not enough understanding of:


	when to use and (more importantly) not to use a button
	what button type is most appropriate
	where to place a button on a page.


Throw in the human need to want to please people (such as when the content designer does exactly what is requested on a ticket) and things can go really wrong. This is where training comes in – to help them question any request we get in terms of the real value it will deliver for our users. After all, we’re here to represent them.


The need for good design choices


So, to clarify our position, this is about how a design system, made up of well-chosen components and patterns, does not replace the need for good design decisions. There are a million ways to make bad choices using good components and patterns – in the same way a piece of text can follow a style guide but still not read well or give users what they need.

The precedent for this in government is interesting.&nbsp;The UK government’s design system&nbsp;and&nbsp;prototyping toolkit&nbsp;are both solid and allow for little variety – you can only prototype in code, so there’s limited potential to deviate, all the way through from sketching to development. Even with all that, there’s still the need for hefty and sophisticated design reviews and assessments to make sure teams are making good decisions. This is because there’s still a lot of room for poor usage.


Question everything everywhere all at once


Getting the whole team on the same page is important, especially around the purpose of the system and how to use it. We’d spent many meetings in the past going back and forth about whether we needed more guidance for our team or for the disciplines we collaborate with. We wanted to make sure we were crystal clear about this as a group, so we have plans to create more training and simple guidance to ensure the wider team understands how to design for specific goals and journeys.

Question everything, everywhere and all at once to get out of your comfort zone and ensure that the people who are interacting with your services get the best experience when that happens. Being comfortable with awkward conversations is important, and it gets easier as you become more familiar with the work, design principles and flows.

We’re iterating as we go along. We’re not over buttons yet. They have a role to play, just not in every page or journey…

Ayala Gordon is associate director of digital user experience at University of Southampton.

This is an edited version of the post “Confessions of a button hater”, which was first published on the University of Southampton’s Digital Team Blog.

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Content design guidance for university UX teams

A design system can underpin consistency and content standards in university website and digital services, but it does not replace good design decisions. Here, Ayala Gordon offers guidance to content design teams 

Why buttons press our buttons

To help higher education professionals understand, in practical terms, how generative&nbsp;AI could support their work, we&nbsp;are presenting 100 applications of ChatGPT over a five-part series. We have already shared an&nbsp;introduction to prompting generative&nbsp;AI for teaching and learning, followed by 25&nbsp;prompts relating to teaching and assessment and&nbsp;25&nbsp;to&nbsp;support administrative tasks. Here, we focus on the AI tool’s potential to enhance student engagement. Below, you will find 25 examples of prompts that show how generative AI can help keep students interested, connected and motivated.

1. Summarise long-read articles and texts: Summarise this research paper on “Students as partners” [insert up to 3,000 words]. Give me a list of dot points and include in-text references and a reference list.&nbsp;

2. Generate study materials, resources and in-class activities: Can you create study materials and resources for a third-year course in thermodynamics, including and in-class prompts for high-achieving and low-achieving students? Develop a specific resource for both cohorts.&nbsp;

3. Provide historical context: Provide historical context for the development of psychology. Outline the primary schools of thought for a first-year psychology course. Outline in chronological order, with the leading theorist of each thought identified. Provide a table of such and leave some answers blank. Provide solutions upside down for students to check their answers. Furthermore, generate story prompts from this to develop a new intro video for students to get them interested in the history of psychology. Make it engaging.&nbsp;


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4. Generate social media strategies and student marketing campaigns for an upcoming event: Can you create a plan to increase student attendance for this extracurricular event on student employability? Identify the resources, timing and planning of the event that is due to be delivered in March 2024. Now, with the list of 18&nbsp;recommendations provided above, develop a detailed plan and timing for each and put it into an Excel output file to share with others.&nbsp;

5. Develop interactive presentations and alternatives:&nbsp;Develop a slide deck on authentic assessment for a professional development workshop. Include headings and subheadings to each slide, and include references and five dot points for each slide. Identify the best font, size and format. Also, list a series of PowerPoint alternatives and online websites such as Canva and Prezi. Now, identify which ones are free to use.&nbsp;

6. Evaluate online learning resources tailored for universal design (UD): Ensure that my study materials on academic integrity [insert your own resource] meet universal design principles using the Centre for Applied Special Technology (CAST) principles of universal design.&nbsp;

7. Provide homework support to students or as an academic adviser: Act as an educational support adviser for students and provide support for students on the issue of marginal utility theory and explain it more simply and succinctly for a ninth-grade student. Develop a cheat sheet guide to different types of utility theory and compile a series of 10 questions and answers to help guide students.&nbsp;

8. Create simulations or games: Develop a learning game or simulations on any particular issue.&nbsp;

9. Develop virtual classroom breakout room activities: Develop a series of activities on ethics in health tailored for breakout rooms in Zoom, Teams or Blackboard Class, which are short but require collaborative input from all students.&nbsp;

10. Develop classroom-management strategies:&nbsp;Function as an academic teaching first-year master’s students in applied linguistics from culturally and linguistically diverse backgrounds. Set up a week-by-week strategy to help international students engage, collaborate, participate and be inclusive.

11. Create an engaging student-facing public speech: Create an engaging and informative script that leverages generative AI capabilities to enhance student engagement. Design the script to capture and maintain student interest in a topic related to work-integrated learning placement opportunities to all first-year students. Incorporate strategies for active participation and interactivity. Conclude with a thought-provoking question or discussion prompt to stimulate classroom engagement and dialogue.&nbsp;

12. Student Excel assistance: Can you help me grasp the concept of Excel’s data validation feature? Could you provide guidance on recording and tweaking macros in Excel? Can you show me how to efficiently organise, analyse and evaluate my schoolwork using Excel, especially when it comes to spotting trends?&nbsp;

13. Provide an essay outline for this issue: Act as a lecturer and provide an outline for any essay on [insert details] and what are the 20 most important things to consider when developing an essay.&nbsp;

14. Create and solve maths problems: Use this regression of y&nbsp;=&nbsp;3x&nbsp;+&nbsp;15 and act as a maths teacher to help identify the main issues students have with this equation. Provide an explanation for each step and provide real-world examples of how this formula is important.&nbsp;

15. Teaching writing skills: Help my students understand the difference between active and passive voice and how syntax and structure can improve their writing; here is an example paragraph, identify explicitly where they need to improve on and how to do it.&nbsp;

16. Algorithmic decision-making: Help my students make better decisions using AI. Suggest that they are an academic manager facing a difficult decision in the university. Use this scenario and generate a list of potential solutions, explaining the underlying algorithm used to determine these options.&nbsp;

17. Quiz error detection and question formatting: Can you identify if my multiple-choice questions are incorrect, inconsistent, obvious, repetitive, distracting or double-barrelled? Do not give me answers to the questions; please check the wording of the question itself.&nbsp;

18. Analyse student report writing based on a rubric: Act as a teacher of a first-year business course and critique the following library database search for journal articles assessment from this student [ChatGPT derived] using this rubric provided by Flinders University. Advise how the student could improve their writing to address assessment requirements adequately. [You can ask students to paste a couple of paragraphs from their essay or report into generative AI to analyse the writing.]&nbsp;

19. Ideas on responding to discussion posts: Respond to this student’s remark on the discussion post and offer three ways of looking at the problem. Be friendly and casual in your tone. Be supportive of their posts and outline a sentence that was interesting. Develop a follow-up question based on the post to generate more discussion for others to reply.&nbsp;

20. Gamification: Assist me in designing games tailored for the course [please specify the course name and context]. Provide illustrative examples, formulate detailed lesson plans and enumerate tasks for both instructors and students. Additionally, highlight critical considerations to ensure the successful execution and integration of these games.&nbsp;

21. Provide instruction on assignments: Help me develop a list of instructions for the following task [specify the task] for my undergraduate students, the majority of whom speak English as a second language. As well as generating ideas, instruct students on homework for the following week and the list of learning outcomes relating to this task.&nbsp;

22. Support with online technologies: Help my students understand the following concept on civil engineering using AutoCAD. Explain the steps they need to follow on the program to get them started, including how to download, install and edit using the tool.&nbsp;

23. Foster creative writing: Can you generate 10 unique writing prompts or story ideas to inspire students’ creativity and help them overcome writer’s block on any particular issue?&nbsp;

24. Summarise YouTube lectures and videos: Take this YouTube transcript from this one-hour professional development workshop and identify what are the 10 main key points. Now, turn these into questions in dot points for a presentation.&nbsp;

25. Unpack terminology and public discourse: Summarise the current understandings of neoliberalism and offer several perspectives and political ideologies relating to this provocative term.&nbsp;

In our next resource in this five-part series, we will share 25 uses of generative AI that can support research and the scholarship of teaching and learning. Please note that prompts and applications of AI output should adhere to the fundamental values of the International Centre for Academic Integrity: honesty, trust, fairness, respect, responsibility and courage.

Seb Dianati is an academic lead for digital learning initiatives, and Suman Laudari is a digital learning designer, both at Charles Darwin University.

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In the fourth part of their series looking at 100 ways to use ChatGPT in higher education, Seb Dianati and Suman Laudari share 25 prompts for the AI tool to boost student engagement

ChatGPT and generative AI: 25 applications to support student engagement

In today’s fast-paced academic landscape, scholars face a rapidly expanding ocean of research articles, so they need search tools capable of more than just fetching documents. The future of search is not just about finding what we seek; it’s about generating deep insights that help us leverage this vast sea of knowledge.

At Elsevier, we are working with the academic community to co-create search engines to meet the scholarly needs of our community.


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We’re entering an era where large language models (LLMs) have the potential to revolutionise the way we search and discover information. When paired with trusted academic databases, these models promise to become more than just text generators; they transform into advanced knowledge engines. They can sift through massive databases, draw connections between disparate fields and even suggest new avenues of research. In this way, they don’t just find information but also help us to understand its broader context and significance, unlocking the full potential of academic resources.

Take Microsoft’s Bing ChatGPT as an example. It demonstrates an impressive application of LLMs in search, but this only scratches the surface of what’s possible. Generating summaries or answers based on web results is helpful. Still, the true value of LLMs lies not in their ability to find information but rather in their capacity to reframe it in a way that helps us to comprehend its significance.&nbsp;

Picture an academic philosopher deeply immersed in research on ethics and values. Through the power of LLMs, a platform could provide thought-provoking perspectives and suggest how research in the field of, say, synthetic biology applies to their work. A biochemist could find their research enriched by insights from quantum computing. The French Revolution could be illuminated to historians through the lens of game theory. This isn’t sci-fi or fantasy; it’s what domainless LLMs can bring to academic enquiry today.

Practical ways to use large language models for reflection

The stakes couldn’t be higher. As knowledge expands, researchers must narrow their focus and master their domains’ growing specialised language. We are locking ourselves into ever-narrowing disciplinary vocabularies and worldviews, as the necessity of remaining experts in our fields truncates our capacity for broad-mindedness. Consider economics’ heavy reliance on mathematical models and statistical analyses, which can create a barrier for those without a strong background in either subject. This leaves the rest of us on the outside looking in, unable to access knowledge that otherwise could help us to create new insights or make informed decisions.&nbsp;

The implications of increasing specialisation are worrying because some of the most remarkable breakthroughs in human history have come from the confluence of disparate fields. One of my favourite examples is the Massachusetts Institute of Technology’s Building&nbsp;20, where researchers, scientists and even janitors roamed each other’s labs, sharing ideas and inspiring bold new concepts. Interdisciplinary collaboration led to groundbreaking innovations in high-speed photography, microwave physics and even the creation of the Bose Corporation. This creative melting pot, which stood from 1943 to 1998, gave birth to the first video game and pioneering research on Chomskyan linguistics.&nbsp;

With LLMs, we have the potential to provide a Building&nbsp;20 for everyone.&nbsp;

One practical step academics and researchers can take now is to start experimenting with LLMs for self-reflective exploration. These models can serve as an intellectual sounding board, capable of generating Socratic-style questions. For instance, if you’re a physicist studying particle behaviour, a well-designed query to an LLM could prompt questions such as: “How might my understanding of particles contribute to advances in medical imaging?” or “Could my research have implications for environmental science?” Such questions not only foster interdisciplinary thinking but also offer a form of self-study and way to broaden academic horizons.

Limitations of large language models for academic enquiry

It’s crucial to note that while LLMs offer much, they are not a cure-all. LLMs, by default, are designed to generate convincing-sounding text, not to produce factual statements often referred to as “hallucinations”. Second, these models are constrained by the data they were trained on, meaning that their “knowledge” can quickly become outdated, especially in fast-evolving disciplines – a critical point, given that the cornerstone of scientific research is accuracy and timeliness.

That said, even these limitations can be harnessed creatively. For example, hallucinations could be instrumental in sparking new connections or theories between disciplines when used judiciously, turning a potential weakness into a novel form of enquiry.

LLMs represent the first significant advancement in search and discovery in decades. Best of all, this is only the beginning, and we are starting to see fine-tuned optimisations already.&nbsp;

For example, we recently released an early version of&nbsp;Scopus&nbsp;AI&nbsp;for researcher testing. This next-generation tool combines generative AI with Scopus’ content and data to help researchers get deeper insights faster and support collaboration.

LLMs will enable us to go beyond mere recall, fostering contextual understanding and innovative discovery. Our goal should not just be to build better search engines but to craft more intelligent, intuitive tools that enrich the pursuit of knowledge itself.

Adrian Raudaschl is a senior product manager at Elsevier.

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How large language models will shape search

The true potential of generative AI and large language models remains underexplored in academia. These technologies may offer more than just answers. Here’s how the insights they offer could revolutionise academic search and discovery

Breaking academic barriers: language models and the future of search

The generative AI-enabled era of learning design, where gen-AI tools are reshaping the way we teach and learn, has ushered in a new era of possibilities for course design. However, as with any new tool, there is a learning curve, and one aspect of gen-AI that requires careful consideration is how we write effective prompts. So, how can we maximise the efficacy of gen-AI outputs?

Unlocking the power of reusable prompts

When we first start using generative AI tools, we often find ourselves engaged in a back-and-forth conversation, fine-tuning prompts until we elicit the desired response. But using gen-AI through trial and error is a time-consuming process that may yield inconsistent outcomes. There exists a more efficient approach to prompt interaction – one that involves deliberate experimentation with various prompt combinations to create reusable prompts. 


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Reusable prompts are single, longer prompts that are adaptable across multiple courses and contexts by merely swapping out specific course details while retaining the core prompt structure. In several instances, through developing reusable prompts, we have been able to distil a complex set of 10 or more prompts into a concise, focused command that achieves consistent results. This not only saves invaluable time and effort but streamlines our interactions. It also gives us the opportunity to share prompts and collaboratively refine them.&nbsp; 

Creating your own reusable prompts

So, how can you create your own reusable prompts? Here is a process to get you started:

1. Select a use case: Consider the various scenarios in which you might use generative AI. These could range from content creation to assessment design or even administrative tasks. Defining a specific use case will help you craft an effective prompt.

2. Identify core elements: Begin by identifying the essential components that need to be included in your prompts. These elements could include the context, objectives, specific requirements or constraints of the task you want the AI to assist you with.

3. Experiment with variations: Start by crafting a few different prompt variations for a specific task. Experiment with wording, structure and format to see how the AI responds to different inputs. This experimentation phase is crucial for refining your prompts over time.

4. Test and refine: Interact with the AI using your initial prompts and observe the generated outputs. Analyse the results and iteratively refine your prompts based on the AI’s responses. Pay attention to what works well and what doesn’t.

5. Focus on reusability: Test your prompt by using different contextual information. Aim to create prompts that can be adapted for different tasks or courses by making only minor adjustments. Think about creating prompts with a flexible structure that allows you to swap out specific details while maintaining the overall format.

6. Record: Once you have a collection of successful prompts, create a repository or a document where you can easily access and modify these prompts as needed.

7. Share and collaborate: Consider sharing your reusable prompts with colleagues or the broader community. Collaboration can lead to further refinement and the discovery of new use cases for generative AI in learning design.

8. Iterate: Learning design and generative AI capabilities are constantly evolving. Regularly revisit and update your reusable prompts to ensure they remain effective and aligned with current needs and trends.

Once you have developed a few reusable prompts with a consistent structure, try sharing them with a gen-AI and asking it to develop others. 

Example of a ready-to-use prompt

To set you on the path of harnessing the power of prompt engineering, here is an example of a ready-to-use prompt for you to experiment with. Simply copy and paste the italicised text below, then replace the content between the square brackets. You might even try this and notice areas where you can refine it.

Design an authentic assessment for [course name]. The authentic assessments should mirror real-world tasks and challenges relevant to the course. 

The assessment should align with specific course learning outcomes and engage learners in practical application. The course learning outcomes are [course learning outcome 1], [course learning outcome 2] and [course learning outcome 3]. The level of the course is [paste in the relevant AQF level criteria].

Tailor the assessments to the course’s level and audience, considering the [course topics, content, aims, etc].

Write an explicit and clear set of student instructions for the assessment. These instructions should include the assessment: purpose (how it helps students achieve the course learning outcomes); description (answering the following questions: How does this task relate to other assessment tasks in the course? What product is the learner required to create? What course content is most significant to this assessment task?); instructions (numbered instructions that guide the learner to complete the task. Begin each instruction with an “action” verb. Include the following: Course content or learning activities/resources that the learner should participate in/revise/refer to before beginning the assessment task, specific questions or points which must be addressed in the task, suggestions and/or requirements regarding section headings, referencing and formatting).

Develop a rubric that outlines criteria for evaluating the assessment against [course learning outcome 1], [course learning outcome 2] and [course learning outcome 3].

Reusable prompts are a pivotal technique in using generative AI to support learning design. Through these carefully crafted prompts, we can remove some of the guesswork from gen-AI interactions to develop consistent, quality and replicable results. However, as with all uses of generative AI, reusable prompts are no substitute for human oversight of gen-AI outputs. All outputs of gen-AI should be vetted and refined by human actors. 

Richard McInnes is learning designer and product lead, and Ajay Kulkarni is digital educational developer, both at the University of Adelaide, Australia.

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Richard McInnes and Ajay Kulkarni discuss how to get consistent quality and replicable results when using generative AI to support course design and development

Mastering generative AI: crafting reusable prompts for effective learning design

It is widely accepted that generative artificial intelligence (AI) such as ChatGPT has huge potential to support the work of academics and higher education professionals. But many are still unsure how to put this into practice. In our five-part series, we have set out to explore 100 applications of these AI tools in higher education.&nbsp;We have shared an introduction to prompting generative AI for teaching and learning followed by 25 prompts relating to teaching and assessment. Here, we turn to administrative work. Below you will find 25 examples of prompts that show how generative AI can help university staff with their administrative duties.

1. Student email correspondence: Write a welcome message to new students. Act as an academic for a first-year course in [subject details]. Outline the assessments mentioned here [insert relevant assessments]. Include the importance of the course to their degree [insert learning outcomes]. Include the need for social presence and collaboration within in-class activities.&nbsp;

2. Respond directly to staff on public forums: Respond to this correspondence from my academic supervisor to all staff. Be polite, slightly funny and highly interested in supporting artificial intelligence and academic integrity. Suggest you are interested in the supervisor’s project and open to doing this.&nbsp;


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3. Write a letter of recommendation: Write a letter of recommendation as a senior university lecturer at an Australian university for a previous student called John Smith. Outline his exceptional social, academic and professional competence.&nbsp;Put it in dot points so I can do the rest.&nbsp;

4. Assist with job interview preparation: Can you help me in preparing for an interview for a level A (assistant professor/associate lecturer) role at this university, for this role [insert job description]? Reference the university’s student body and nuances to teaching and learning, their values and strategic plan, and use this selection criterion. 

Develop a cover letter for this role using this résumé [copy and paste the main parts of your résumé] and their job advertisement [copy and paste parts of the job advertisement]. Be quirky, engaging and funny in the last sentence. Now give me 10 common questions for this role.&nbsp;

5. Feedback on a résumé or cover letter: Can you review a cover letter based on a specific role and offer feedback on whether I have addressed the main responsibilities of the job?&nbsp;

6. Generate writing prompts: Provide students with a series of writing prompts on decolonising the curriculum. Offer some that are easy, medium and hard.&nbsp;

7. Create reading lists: Develop a reading list on this issue of Indigenous education in Australia over the past 10 years. Get students to analyse the accuracy and validity of the reading lists provided by the chat.&nbsp;

8. Provide YouTube summaries: Use this YouTube transcript to offer 10 dot points of the main points discussed at the conference on [insert details] issue.&nbsp;

9. Develop diverse teams: Take this list of 20 students and their first score in the class and best develop groups from the course to offer teams with varied levels of academic performance in each group.&nbsp;

10. Provide support for special needs students on student access plans: Use this students’ access and accommodation plan and provide guidance for how I can best support this student. Offer nuanced solutions.&nbsp;

11. Develop email rules: Develop a list of email etiquette rules for students when emailing staff, including tone, formatting and appropriate language for communication. Now turn this into a post on the learning management system.&nbsp;

12. Provide training for classroom peer assisted study support students (PASS): Provide a list of activities for students to work with other students. Give PASS students learning materials on universal design and strength-based pedagogical approaches to help other students. Develop a pre- and post-support plan for new PASS student tutors.&nbsp;

13. Develop ways to incorporate technology: Provide a list of 10 proven evidence-based strategies that can support the implementation of technological tools to enhance my teaching and learning practices for my students. Now offer technological tools to help implement the above.&nbsp;

14. Provide feedback on student assignments: If generative AI is implemented to your learning management system (LMS), provide feedback to students, whereby students are enrolled in a hypothetical computer science course and the AI receives their assignments and analyses the code and offers feedback. Explain how this could be made possible in other courses and in this example.

15. Develop strategies for classroom inclusivity: Act as an accessibility expert and identify areas of this rubric and this assessment that could be more inclusive. Now provide alternative assessments based on the results. Develop a hypothetical scenario of how generative AI can support the above.&nbsp;

16. Collaborative learning ideas: Offer learning activities online and in class, instead of “think, pair and share” activities, to engage students and encourage them to talk to each other.&nbsp;

17. For academic support: Provide a hypothetical case study of how generative AI could offer academic support to students. Develop a series of questions and answers that might support a student developing an essay.&nbsp;

18. Provide strategies and resources for teaching staff: Act as a coordinator and offer new staff a series of teaching and learning strategies for their small classes. Offer a workbook of things to consider and summarise feedback from last year’s evaluation to improve their delivery.&nbsp;

19. Proofread and edit copy: Review the following piece of writing and correct spelling, grammar, punctuation and syntax errors. Provide five areas of improvement.&nbsp;

20. Develop learning outcomes or learning objectives: List these learning objectives as learning outcomes. Now apply Bloom’s taxonomy to develop higher-order learning outcomes.

21. Unpack and make sense rubrics: Unpack the ratings and criteria of the following rubric. Suggest ideas and recommend steps so that I can achieve high distinction in this assessment item. Encourage students to use AI to unpack and understand the rubric and task requirements. This feature can be helpful for students who struggle to understand the language used in rubrics or for students who want to ensure that they are meeting all the criteria.

22. Support inductions with students as partners: Develop a strategy for students as partners to work collaboratively with staff within induction. Generative AI could offer strategies to decrease power between staff and students. A potential prompt could be developing a team charter for effective group work and allocation of time and work that all students sign.

23. Create a week-by-week study plan using assessment and learning objectives: You are an undergrad student at an Australian university, you have two part-time jobs [specify shifts], and you have classes on these days and times [specify the schedule]. Now generate a study plan for the semester, consisting of 12 weeks. Use this assessment and course material, and weekly learning outcomes, and plan out a week-by-week strategy of what I should be doing.&nbsp;

24. Communicate with students on discussion forums:&nbsp;Assist me in writing correspondence to encourage students to complete or respond to discussion posts or get assistance in writing personalised feedback and reminders. Ensure the tone is encouraging and friendly.

25. Send reminders and notifications: Develop a research plan and project plan for these five projects. Plan and space them out in my calendar and set up reminders, noting not to put any in one specific week.&nbsp;

In our next resource in this five-part series, we will share 25 uses of generative AI that can help to boost student engagement. Please note that prompts and applications of AI output should adhere to the fundamental values of the International Centre for Academic Integrity: honesty, trust, fairness, respect, responsibility and courage.

Seb Dianati is an academic lead for digital learning initiatives, and Suman Laudari is a digital learning designer, both at Charles Darwin University.

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In the third part of their series looking at 100 ways to use ChatGPT in higher education, Seb Dianati and Suman Laudari share 25 prompts for the AI tool to assist with administrative duties

ChatGPT and generative AI: 25 applications to support administrative tasks 

In our commitment to delivering high-quality lifelong learning and massive open online courses (Moocs) that are widely accessible at little or no cost, we have embraced the use of open educational resources (OERs). The use of OERs in our online course design workflows for the past 10 years has built our understanding of how to harness and share these resources in order to maximise the benefits for both the learner and the creator. Here I share useful insights for anyone wishing to work with OERs.Encourage the use and creation of OERsWe advise all teams creating short online courses that both the assets they create and the resources they include in their courses should be OERs. This can, at times, be challenging because there are sometimes limits on what can be freely shared. But online repositories such as TES Resources, Merlot, OERCommons, Pixaby and OpenLearn Create provide a huge range of OERs that can be used as learning and support materials in new courses.If you are able to make your own open materials, make it a worthwhile investment by creating an institutional repository for OERs. This will encourage other staff and students to embrace OERs and add to your collection.To encourage this, our workflows include the addition of all media created to our Open Media Bank. This currently holds more than 1,200 pieces of high-quality media created for short online courses, which are freely available for anyone, anywhere in the world to adapt, reuse and reshare.Eyes on the horizon collection: Innovations in providing higher educationTHE podcast: the big ideas behind microcredentialsResources on lifelong learningOur OERs are not only created by our media team. We have a successful internship programme through which student interns help build our portfolio of OERs. This allows students to gain valuable work experience and understand what OERs are and how they might use them. It enables our team to connect with students and understand what drives them in their studies and career aspirations.Over the past year, our media interns have helped film, edit and upload to Pexels, a website for free stock photos and videos – we have added more than 69 stock footage assets, which have been downloaded more than 6,000 times so far in 2023.Explore new routes to accessible learningContent we create for Moocs, such as podcasts, videos, photographs, guides, is shared under an open licence, to remove barriers of having it behind a paywall. We strive to ensure that all course resources such as articles are openly licensed. More often than not, even if an original resource is copyrighted, you can find an openly licensed alternative.When hosted on learning platforms, online courses may incur a fee. We try to overcome this barrier by making it clear on our website that the media content featured in these courses is freely available to access via our Open Media Bank, to work through at students’ own pace. It can also be addressed by working with platform partners to find out which one has the most free-to-access options.Make sure you have a thorough understanding of the potential barriers to delivering courses on a paid platform, such as the cost of completion certificates that permit ongoing access to a course. You can then start to explore ways to work around this to make your materials accessible. We have been experimenting with the creation of e-books from the course materials. These will be freely available on Edinburgh Diamond, our in-house platform that supports the publication of academic and student-led open access books and journals. Our first e-book, Fundamentals of Music Theory, was developed from a successful Mooc hosted on Coursera, which has been running since 2012. The e-book, launched after a course refresh in 2022, has had more than 10,000 downloads.Exploring new avenues of dissemination can be daunting but it is a great way to experiment and learn, to upskill staff and to develop new ways to reduce or remove the cost of learning in order to reach new audiences.Build up your badgesAnother area for exploration is recognition of learning through digital badges. Digital badges enable students to share a wide range of verifiable learning and professional achievements across different platforms. They help to demonstrate achievements for learning outside of a degree, such as through short online courses, extracurricular activities, professional skills development and more.While digital badges, as an alternative form of recognition for learning, have gained little traction until recently, we believe they are here to stay. They are portable and easily visible to potential employers when added to online profiles on websites such as LinkedIn and X. They recognise valuable learning that is not associated with formal university credits.But such badges must offer genuine value. We are piloting a robust governance process to ensure our badges demonstrate clear value for the learner. Requests to create a badge are peer reviewed and the impetus is to ensure potential employers understand why skills or competencies demonstrated have gained such recognition.By investing in staff resources and a digital badging platform, institutions can provide learners with a tool to demonstrate their skills to employers, without incurring a financial burden themselves.In less than six months, 55 badges have been created from seven areas of the university, 1,300 badges have been awarded and shared more than 600 times on platforms such as LinkedIn, Twitter and Instagram. This demonstrates a clear demand for this form of recognition.Commitment and courage for positive changeWidening access to online resources to all learners requires institutional commitments and investment in time and staffing. Try to find the space to step back and think outside the box in order to explore new avenues for course delivery. By daring to try something new, you can bring positive lasting impacts to non-traditional learners, staff and students who deserve the recognition for their input.Finally, learn from others. You might not need to reinvent the wheel. There is great value in speaking to other institutions to find out what they have done and then adapting it to fit your needs.Fiona Buckland is learning technology team manager at the University of Edinburgh.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus&nbsp;newsletter.

How open educational resources can be created and managed to ensure courses and materials that support lifelong learning are accessible to all aspiring students at little or no cost

Open educational resources to make lifelong learning accessible to all

A significant proportion of academic journals are published in English, and many international academic conferences are also conducted in English. As such, non-native English speakers often face difficulties in preparing manuscripts in acceptable professional English that pertain to their research areas. Even for native English speakers such as myself, manuscript preparation will not always be free of grammatical errors. 

Sometimes, achieving the expected degree of clarity and coherence in our writing can be challenging. In this article, I will share my personal experience with ChatGPT and how it has shaped my academic writings, and I will address some of the ethical concerns associated with using AI language models such as ChatGPT in manuscript preparation. 


	Collection:&nbsp;AI transformers like ChatGPT are here, so what next?
	Should we trust students in the age of generative AI?
	Embracing generative AI as a co-design partner for designing and developing courses


When I was first introduced to ChatGPT, I was immediately fascinated by its capability for assisting with improving writing clarity. When preparing manuscripts, I often found it difficult to write flawlessly because my cognitive processing speed exceeded my reading or writing speed. This can cause, for example, the skipping of words in sections that I have already processed cognitively. ChatGPT has proved to be a valuable tool, helping me to enhance the quality of my writing by providing suggestions, refining sentence structures and offering alternatives to missing or specific words. 

To make the most of ChatGPT, I explored various resources and online communities to learn about its capabilities and how best to use it. By combining my own knowledge and experience with ChatGPT’s assistance, I have minimised typographical and grammatical errors significantly and improved the clarity and coherence of my academic writing.

While ChatGPT appears promising for helping to improve manuscript clarity and minimise errors, without proper human supervision the output might contain elements of AI-generated text. This has raised concerns and diminished the credibility of some research manuscript outputs. 

The misuse of AI-generated text has led to the development of similarity checkers to detect text generated by AI. Using ChatGPT solely for proofreading purposes could introduce AI-generated text if there is a lack of necessary human oversight, because the AI is able to change words or phrases based on its trained algorithm, which could jeopardise the credibility of the author. 

To help address the issue of AI-generated text when using ChatGPT for manuscript editing services, users must provide specific instructions (prompts) that prevent the algorithm from generating AI text. 

First, users will need to understand the characteristics of language and writing, including tone, style, clarity, syntax, register, dialect and domain. To improve the manuscript, the ChatGPT algorithm is trained to replace words or phrases that occur frequently in a sequence of words. This is based on the combinations of words or phrases that are contained in the algorithm’s training dataset. In essence, ChatGPT mimics the arrangement of words or phrases that most writers use often. However, human writing may, of course, include words or phrases that occur less frequently. 

By incorporating the following approach, researchers can maintain both clarity and ethical standards.

To improve clarity, instruct ChatGPT to prioritise using words or phrases that occur with medium-to-low probability in the algorithm’s training dataset. This helps maintain human writing intent and author’s voice, while also enhancing the overall quality of the text. 

Further approaches for maintaining the ethical credibility of a manuscript when using ChatGPT for editing services include:

1. If you wish to use ChatGPT for editing, including clarity, the instruction should have a phrase such as “for clarity, prioritise words or phrases with lower-probability occurrences in a sequence of words. If not available, use words with medium-probability occurrences.” This provides explicit guidance to ChatGPT, ensuring that the generated text aligns with the desired level of clarity and coherence while avoiding over-reliance on high-probability words.

2. Or you can ask ChatGPT to “improve the clarity of my manuscript by suggesting alternative words or phrases”. You can then selectively accept or reject these suggestions, giving you the flexibility to maintain control over the content while also leveraging the AI model’s capabilities.

An example of the prompt given to ChatGPT for proofreading this piece are as follows:

Request: Proofread and improve clarity of the manuscript. 

Context: For clarity improvement, use only medium-probability-occurring words or phrases for generating the next word in a sequence of words.

Tone and domain: Academic magazine.

Language: Use British English.

As we learn more about the relationship between the effectiveness of ChatGPT and the quality of user-provided prompts, it becomes clear that careful attention to prompt construction is essential for optimising manuscripts and ethical integrity. Specifically, aligning the purpose, target audience, desired outcome and constraints of your ChatGPT prompt with your academic writing goals will help you create a more coherent and ethically sound manuscript. The level of detail and specificity in the prompt can also significantly influence the text generated by ChatGPT, so it is important to have human oversight to ensure ethical credibility.

By strategically designing prompts that are aligned with your research goals and employing guidelines for maintaining ethical standards, such as instructing ChatGPT to prioritise medium-to-low probability language structures, researchers can effectively harness ChatGPT’s capabilities to enhance both the clarity and the ethical standing of their academic writings.

ChatGPT is a powerful tool, but it is important to use it carefully and ethically. By providing specific instructions to ChatGPT and carefully reviewing the output, researchers can minimise the risk of introducing AI-generated text into their manuscripts.

Ikenna Uwanuakwa is an assistant professor teaching in the Department of Civil Engineering at Near East University in North Cyprus.

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Ikenna Uwanuakwa shares experience and tips for using ChatGPT to improve academic writing without falling foul of ethical considerations

Enhancing manuscript clarity and ethical credibility with ChatGPT

The conversation regarding academic freedom and free speech has reached fever pitch over the last five years. The cycle of controversy and “cancellation” seems to jump from one issue to the next at lightning speed. With academics, students and institutions clashing over polarising issues such as free speech, “cancel culture”, gender rights and a febrile political climate, academic freedom feels more topical now than in previous decades.

At the same time, the learning process itself faces disruption. Generative artificial intelligence (AI) such as ChatGPT has the potential to change the way people learn, read and write. Within a decade, AI will be judged to be as significant an advance as the transistor or the birth control pill. It is a revolutionary technology, and a double-edged sword.

AI is biased. Developers subconsciously impart their societal views as they create these tools, then the language models that make up programmes such as ChatGPT encourage word association, which reflects our societal biases back at us. The famous example of Microsoft’s short-lived AI chatbot, Tay, showed us this in 2016 after it had to be taken down when it began to deny the Holocaust less than a day after it was launched. More recently, in the US, people have been wrongfully arrested due to AI facial recognition systems, particularly African-Americans. 


	Resource collection:&nbsp;What can universities do to protect academic freedom?
	Resource collection:&nbsp;AI transformers like ChatGPT are here, so what next?
	Resource collection:&nbsp;Higher education’s role in upholding democracy


All this creates a real issue for higher education and academics. It’s vital that the next generation of students are taught how to think rather than what to think, as AI risks us doing. The ability of tools such as ChatGPT to provide answers and, theoretically, help us decide what’s true, is hugely convenient and must be incredibly tempting for students struggling with certain subjects. 

But we must keep alive the importance of learning for its own sake, as well as critical thinking. It’s vital that students know that they must question what AI tells them and form their own opinions, rather than let biased AI models cherry pick facts to lead them to pre-determined opinions. 

As academics and as a sector we need to consider carefully how to adapt to AI while maintaining our core beliefs, chiefly academic freedom and free speech. AI tools such as ChatGPT inherently feel positive for education. People can find out almost anything they want in seconds, making education accessible and more easily understandable to many. This is not some evil we must try to dodge – even if we could – but a new way of learning that we must integrate with other methods without overwhelming them.

First, we should be creative in matters of academic freedom and regarding the integrity of academic work. Discussions about in-person and oral exams or video personal statements allow individuality to be taken into account and lessen the chance of AI tools being used negatively. We must be innovative in how we measure knowledge and ensure we encourage a plurality of students with a variety of different opinions. 

On a practical level, this means ensuring both academics and students are “AI literate”. We have to accept that students will use AI, much in the same way as 20 years ago there were concerns around the internet and the use of Wikipedia, for example. But by encouraging students to use AI to debate arguments, check spelling and carry out other administrative tasks, we can integrate ways of using it helpfully, while making it clear that relying on AI to form opinions is verboten.

Second, we must remember that, although AI is powerful, it’s not omnipotent. We need to continue to develop AI in ways that ensure it helps to protect and promote academic freedom, rather than becoming a tool used to suppress it. 

Academics can work through their institutions to lobby business and government to request that AI development happens at a responsible and manageable level, rather than as a race to the most powerful AI programmes possible. We should urge our institutions to consider new courses and programmes in AI development and prompting so students understand in more detail exactly how AI can be used.

Moreover, AI is not perfect. The true curiosity of learning for its own sake, for debate and argument, cannot be fully replicated by AI, no matter how smart. The human touch is important, and it’s important we don’t denigrate our own chances of preserving academic freedom. 

Practically, this means a few things. Academics must support any colleagues whose academic freedom is curtailed when discussing AI – such as the recent sacking of Timnit Gebru, the co-lead of Google’s ethical AI team, who was forced out after she authored papers that raised some awkward questions about Google’s advancement of AI programs. 

Colleagues must rally round academics who are attacked for their research. We should go out of our way to offer platforms to academics who have been censored like Timnit Gebru. We should support our academic colleagues who are actively researching AI in other ways too, offering mentorship and giving space in academic journals to papers researching the ethics of AI and academic freedom. 

We should lobby representatives in government for financial support for such research wherever possible – the recent UK government’s £54 million funding for this is welcome but a drop in the ocean for a potentially era-defining issue. We can all do our best as academics and experts in our fields to lobby MPs for further support. With the potential economic gains AI can offer, this funding should be viewed as an investment.

Finally, we should remember that, although AI is revolutionary, the battle between authority and academic freedom is not new. From Galileo’s persecution for insisting the sun was the centre of the universe through to oppression of political opinion in many parts of the world today, such disputes have been ebbing and flowing for centuries. Misinformation, suppression and the importance of free speech have a long history. AI is a new front in this dispute, which we can’t yet fully comprehend. But with the right attitude and incentives, we can work to make AI a revolutionary tool for academic freedom and education, rather than a hurdle to be confronted.

Shweta Singh is assistant professor in information systems and management at Warwick Business School, University of Warwick.

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How academics can manage and guide the use of generative artificial intelligence such as ChatGPT so that it enhances learning and independent thought and does not hamper academic freedom

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