While some forms of assessment may be better able to withstand the threat posed by Gen AI, many forms of assessment tasks are at risk. There is a need to review assessments and, where they are at risk, redesign these so that students can, with integrity, demonstrate their learning attainment in an AI-enabled landscape.
Approaches that are not sustainable:
- Strategies to ignore or catch out students using GenAI to cheat in assessment
- A programme wide use of invigilated in-person exams (which is neither practical nor inclusive (Dawson et al., 2024)
Approaches that are sustainable:
- Supporting staff to redesign assessments to support greater assessment validity (Dawson et al., 2024)
- Supporting students to develop ethical approaches to AI in assessment through guided uses of Gen AI in class learning activities and assessments (Bearman et al., 2024)
- Identifying and responding to reasons why students engage in ‘cognitive offloading’ via use of Gen AI (Dawson, 2020)
- Focusing attention on the process of learning and on process-based assessment strategies that uncover “evidence as to whether or not the work of learning has occurred” (Ellis & Lodge, 2024)
- Taking a holistic view of assessment which includes fewer assessment tasks, securing some assessments better, and taking a programme-based approach to collecting evidence of learning (Dawson, 2020)
AUT has created a university-wide assessment framework to support a model of sustainable use of Gen AI in assessment for both students and staff.
Gen AI in assessment at AUT
Assessment sits in one of two channels.
Channel 1 - AI tools cannot be used
- Secured, controlled assessment tasks where it may be decided that Gen AI tools cannot be used.
- Programme and course teams will identify the key assessment moments in a programme where the assessment of learning in secure settings is required.
Examples might include viva/interactive oral presentations, supervised on-campus exams, clinical observations.
Channel 2 - AI tools can be used
- The appropriate use of Gen AI is supported through assessment design.
- The role and contribution of AI tools to the assessment process and product will be identified.
The role that Gen AI will play in each assessment task must be considered.
References and further reading
- Bearman, M., Tai, J., Dawson, P., Boud, D., & Ajjawi, R. (2024). Developing evaluative judgement for a time of generative artificial intelligence. Assessment & Evaluation in Higher Education, 1–13. https://doi.org/10.1080/02602938.2024.2335321
- Dawson, P. (2020). Defending Assessment Security in a Digital World. Preventing E-Cheating and Supporting Academic Integrity in Higher Education. Routledge. https://doi.org/10.4324/9780429324178
- Dawson, P., Bearman, M., Dollinger, M., & Boud, D. (2024). Validity matters more than cheating. Assessment & Evaluation in Higher Education, 1–12. https://doi.org/10.1080/02602938.2024.2386662
- Ellis, C. & Lodge, J. (July 9, 2024). Stop looking for evidence of cheating with AI and start looking for evidence of learning). LinkedIn . https://www.linkedin.com/pulse/stop-looking-evidence-cheating-ai-start-learning-cath-ellis-h0zzc/
- Lodge, J. M. (2024). AI cheating in education: What can we do right now? https://www.linkedin.com/pulse/ai-cheating-education-what-can-we-do-right-now-jason-m-lodge-ipbtc/
- Perkins, M., Furze, L., Roe, J., & MacVaugh, J. (2024). The Artificial Intelligence Assessment Scale (AIAS): A Framework for Ethical Integration of Generative AI in Educational Assessment. Journal of University Teaching and Learning Practice, 21(06), Article 06. https://doi.org/10.53761/q3azde36
- QAA. (2023). Reconsidering assessment for the Chat GPT era: QAA advice on developing sustainable assessment strategies. https://www.qaa.ac.uk/docs/qaa/members/reconsidering-assessment-for-the-chat-gpt-era.pdf