Six assessment types that support deep, real-world learning

Assessment in higher education continues to rely heavily on traditional methods such as examinations, essays and quizzes. The advantages of these approaches include standardisation, fairness and efficiency, but they are primarily suited to evaluating foundational knowledge and basic analytical skills.

And they have other notable limitations. They tend to reward memorisation and short-term performance over deep learning and long-term skill development. So they might not fully capture important graduate attributes such as creativity, collaboration, problem-solving and application of knowledge real-world contexts. In addition, high-stakes exams can create stress and disadvantage some students, while standardised formats might not adequately accommodate diverse learning styles or backgrounds.

Artificial intelligence tools have introduced further challenges to standard assessment practices, particularly in ensuring academic integrity and accurately assessing students’ independent work.

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Here, I look at six models for future assessment in higher education, drawing on my teaching experience at undergraduate and postgraduate levels. In deciding among approaches, it is important to recognise implementation challenges such as workload and scalability.

Authentic assessment

Authentic assessment requires students to apply their knowledge and skills to tasks that closely reflect real-world contexts and professional practices, rather than focusing on memorisation or abstract exercises. For example, in a public economics course, students might be asked to evaluate a government policy such as a carbon tax.

Tasks could include analysing economic impacts, distributional effects and efficiency implications, and presenting findings as a policy brief for a government agency. This approach develops analytical skills, policy reasoning and the ability to communicate evidence-based recommendations in practical settings and so prepares students for real-world decision-making.

Project-based assessment

Project-based assessment spans a longer time. Students explore a real problem and produce a structured output that demonstrates applied understanding. For example, in a statistics course, students might analyse a real dataset to determine the relationship between variables such as advertising expenditure and sales. The project would involve data preparation, regression analysis, interpretation of results and presentation of findings in a formal report.

This approach enhances deeper learning than do traditional assessments, where students look at well-defined problems under timed conditions. Project-based assessment fosters research skills, independent enquiry and the ability to integrate statistical theory with empirical evidence.

Portfolio assessment

Students compile and reflect on a portfolio of their work collected over time, such as essays, projects, presentations, data analysis tasks and reflective journals. In a development economics course, for example, students might be assessed on a portfolio that tracks their understanding of key issues such as poverty, inequality and economic growth. This moves beyond memorisation of theoretical content and recall-based examination performance, and instead promotes deeper conceptual understanding and stronger application of theory to real-world development problems, alongside enhanced critical thinking, reflection and self-directed learning skills.

Gamified assessment

Game-like elements such as points, levels, challenges and feedback are being used more frequently to make learning interactive, engaging and motivating. This gamification is also used to assess students’ understanding and skills.

In a simulated investment game in a finance course, students might manage a virtual portfolio, make decisions about buying and selling assets and respond to changing market conditions. They are assessed on their financial performance as well as their ability to explain and justify their investment decisions using relevant financial theory. In comparison with traditional assessment, this type of assessment makes learning more interactive and applied while also developing decision-making and problem-solving skills through real-time or simulated scenarios.

Competency-based assessment

Competency-based assessment requires students to apply knowledge and skills in practical contexts, rather than simply recalling information. For example, in a labour economics course, students might be required to analyse wage data, estimate a wage equation and interpret the effects of education or work experience on earnings, which demonstrates their ability to apply economic theory and empirical methods.

AI-generated assessment

This assessment type uses AI tools; students are evaluated on their ability to critically engage with, interpret and improve AI-generated outputs rather than producing answers themselves.

In a business analytics course, for instance, students might be asked to use an AI tool to generate a sales forecast and a short managerial report based on a dataset. The student then evaluates the accuracy of the AI output, checks whether the conclusions are supported by the data, identifies errors or misleading interpretations (such as confusing correlation with causation), and revises the report to produce a more accurate and well-reasoned managerial recommendation.

This improves on traditional assessment in its emphasis on higher-order thinking skills such as critical analysis, evaluation and verification. It reflects real-world practice, where professionals use AI tools to generate insights but also apply their own judgement and domain knowledge to ensure that outputs are valid, reliable and useful for decision-making.

So educators have options to evolve assessment beyond traditional approaches and towards more diverse and innovative forms. A variety of approaches can offer more meaningful and relevant ways to support student learning and success.

Temesgen Kifle is a senior lecturer in the School of Economics at the University of Queensland.

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