Episode 3: The Artificial Intelligence Assessment Scale with Kirsty Duff
Listen now: The Artificial Intelligence Assessment Scale_Kirsty Duff
Written Introduction to Episode 3
How can we guide students to use generative AI responsibly without banning it or letting it “write the assignment”? In this episode, Kirsty Duff (Director of Foundation Studies and Academic Misconduct Officer, University of St Andrews) introduces the Artificial Intelligence Assessment Scale (AIAS) developed by Mike Perkins and colleagues – a five-level framework from no AI to full AI. Kirsty shows how she embeds the AIAS in handbooks and induction activities so that staff and students know what’s acceptable, why, and how to evidence working with AI. You’ll hear concrete, classroom-ready ideas as well as discussion of workload, policy fit, disciplinary differences, and how to move beyond a “deficit” view of AI while keeping integrity central. If you want practical guidance you can adopt tomorrow, this one’s for you. Join us to hear more!
Follow up: Resources Discussed in the Podcast Episode
Perkins, M., Roe, J., & Furze, L. (2025). Reimagining the Artificial Intelligence Assessment Scale (AIAS): A refined framework for educational assessment. Journal of University Teaching and Learning Practice, 22(7). https://leonfurze.com/wp-content/uploads/2025/09/JUTLPFinalPerkins_JUTLP_2025.pdf
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(6), 49–66. https://search.informit.org/doi/10.3316/informit.T2024092900003300954126858
Perkins, M., Roe, J., & Furze, L. (2025). How (not) to use the AI Assessment Scale. Journal of Applied Learning and Teaching, 8(2). https://doi.org/10.37074/jalt.2025.8.2.15
Reflecting on your Own Practice
Policy clarity:
- How will you signal, in handbooks and induction, exactly what AI use is acceptable in each assessment – e.g., mapping tasks to a level on the AI Assessment Scale (from “no AI” to “full AI”)?
Process over product:
- Where could you design activities that foreground how students work (brainstorming, structuring, editing) rather than the final output, to reduce misconduct and build judgement?
Open-book nuance:
- If students may consult notes in open-book exams, how will you address the risk that those notes are AI-generated and disconnected from taught material (e.g., require citation to lecture/seminar sources)?
Equity and choice:
- Given ethical, environmental, and access concerns, will you allow an opt-out pathway for students who prefer not to use AI, without disadvantage? How would you phrase that option?
Discipline fit:
What adaptations would your discipline need (e.g., from essay-focused activities to code, lab, or design tasks) to keep the same principles but change the artefacts?