ChatGPT and learning design: what online content creation opportunities does it offer?

A guide to how ChatGPT and other AI writers can be used to help learning designers and faculty create course content more efficiently



RMIT University
10 Feb 2023
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There has been a lot of debate about how ChatGPT can be used to reimagine the way we teach content and deliver assessment. Most of the focus is about how we can redesign and craft cheat-proof assessments that mostly embrace AI. Higher education is coming to terms with how ChatGPT, and artificial intelligence more broadly, can be embraced by exploiting it as digital aid to improve writing skills and creating assessments that are growth-oriented and incorporate skills-based activities. We’ve seen unexpected and welcome applications such as the potential to make communication and assessment more inclusive for students with disabilities.

In December 2022, the Future Trends Forum hosted by researcher and futurist Bryan Alexander gathered to collectively explore how ChatGPT might reshape academic writing. One of the questions posed during this session was:

In 2023 we will be launching a 100% online master’s course to nine countries on four continents. ChatGPT creates challenges for remote assessment but does it also offer online content creation opportunities? 

The answer is a resounding yes. There is untapped potential to engage with AI in the learning design profession, especially for content creation.

Through generating content in collaboration with AI, learning designers and universities can gain efficiencies in their practice and processes. For learning designers, AI enables them to (1) put time back in hands of time-poor faculty or (2) consider how the efficiencies can be reinvested in value-add tasks that enhance course design. For universities, this cuts down on course build times which can permit them to go to market faster and respond to skills gaps in more agile ways.

Useful examples of where AI can be used to create content during the course design process are in writing the course outline, writing the first draft of course content, and for scripting and editing videos and podcasts. In this piece, we provide a summary of each example followed by considerations and next steps. By doing so, we hope to engage learning designers in considering how their role might start to imagine working with AI.

Using ChatGPT to create online course content: course outlines, writing content, scripting and editing videos

Creating a course outline: Online course development begins by creating an extensive course outline (commonly called a course map or blueprint). Learning designers and academics together sequence topics to divide the course into weekly units that are logical and engaging, and align to the course objectives, assignments and target student cohort.

Creating an initial draft of a course map usually takes a minimum of three hours, often in a workshop setting. ChatGPT can quickly generate a first draft of a course outline to specified learning outcomes, student cohort and assessments, which can then be refined by the learning designer and academic, saving significant development time.

Creating written content: ChatGPT can be used as a research assistant, generating a first draft of each online course page according to topics in a course map, which can then be sent to an academic. This overcomes the “blank page” problem academics often face and is a form of “creative assistance”. Pages can then be reviewed, edited and revised by the academic to ensure accuracy, coherence, and alignment with course objectives and learning outcomes.

AI tools can be trained to create pages of instructional text based on a writing guide, allowing learning designers to focus on other elements of course design such as interactives.

Scripting and editing educational videos: ChatGPT can be prompted to write draft scripts for videos and podcasts or generate questions for interviewing subject matter experts, which can then be refined by learning designers. Following on from this, AI can bring efficiencies to the post-production process. For example, when producing videos with segments from multiple interviews, it can be used to reduce the time spent on editing. This can be achieved by feeding AI tools the transcriptions and prompting it to create a cohesive narrative from the sentences within. The AI output from this can be used to guide content creators during the editing process.

Considerations and next steps

AI is here and it will remain. Learning designers, like the rest of society, will need to examine the benefits and drawbacks in the context of their university community, and view it through the lens of putting the pedagogy before the technology. Some useful consideration and next steps to guide the profession include:

1. Expand your toolbox

Remember, this isn’t just about ChatGPT, there are other AI tools that practitioners need to be engaging with which are being used to produce art, sounds, voices and programming.

2. Remain flexible

Not every learning designer, faculty member or student will want to work with AI and we need to accept this. In his 2019 book Should Robots Replace Teachers? Neil Selwyn advocates that integration of AI into teaching and learning must be presented as choice. We need to be able to present a sound educational justification for the use of AI in education and not just for the sake of change.

3. For those who want to use AI, decide what’s in and what’s out

Start having conversations with faculty about what is within and out of bounds for content creation. A starting point for consideration might be using Bloom’s taxonomy to decide what learning content is generated by AI, the subject matter expert and the student. For example, ChatGPT has demonstrated its acute ability to recall, explain and compare information from sources, whereas the focus from the academic might be real-world application and analysis, and the student creates a new or original piece of work.

4. Learn to be a prompt engineer

Current AI writing technology is best applied to discreet chunks of information, which can be put together then edited and refined. This is ideal for building online courses that are modular in nature. For individual learning designers, AI tools are like having an intern in your pocket capable of generating modules quickly. But just like a human writer, AI needs good prompts to work well. This mindset shift and key skill, akin to learning how to do a Google search efficiently, is best learned by using and experimenting with AI tools.

Universities will continue to embrace AI while looking to shape high-level principles that help us maintain the integrity of the academy. Until that point is reached, we suggest applying the principles from the Sentiment Syllabus Project:

  1. AI should not be able to pass a course
  2. when used AI should be attributed
  3. AI should be open and documented.

Please note: These are the opinions of the authors and not the position of the university.

Dawn Gilmore is an associate professor and academic director at RMIT Online, Anitra Nottingham is head of course design and Marcelo Zerwes is creative media manager, all at RMIT University.

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