Rethinking University Assessment for AI
Redesign module assessments so grades reflect student learning when AI assistants are everywhere
A taste of a lesson
My department wants every module to be AI proof by next year. Is that realistic?
Not really, and trying can push everything into exams, which hurts validity for many outcomes and raises workload and accessibility issues. A more workable aim is programme level: identify the key outcomes that must be verified individually and secure them in a few assessments, such as orals or supervised tasks, while other assessments openly include AI and teach students to use it critically. Which two or three outcomes in your programme would you most need to verify in person?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Map assessments to learning outcomes and decide which must be secure
- Redesign an assessment using oral, process or authentic components
- Write clear student guidance on permitted and acknowledged AI use
- Weigh validity, workload, fairness and accessibility in assessment choices
- Explain why detection tools are an unreliable basis for misconduct cases
Lesson plan
- 1 What does the grade certify? Audit an assessment's alignment with learning outcomes under realistic AI availability. Start
- 2 Secure and open assessment Distinguish assessments that verify individual attainment from those that develop AI capable practice. Start
- 3 Oral, process and authentic designs Compare redesign options and choose one that fits your outcomes, cohort size and resources. Start
- 4 Integrating AI into open tasks Design tasks where students use AI deliberately and are assessed on judgement and critique. Start
- 5 Fairness, detection and evidence Address equity, accessibility and integrity processes honestly. Start
- 6 Communicating the redesign Write student guidance and a rationale colleagues and boards can approve. Start
Try asking
About this tutor
For lecturers, programme leaders and academic integrity officers who need assessment strategies that hold up now that students have capable AI assistants. We move beyond detection toward design: constructive alignment, deciding which assessments must be secure and which can openly integrate AI, authentic tasks, oral and process based assessment, and programme level thinking. You redesign an assessment from your own module, write clear student guidance on permitted AI use, and consider workload, fairness, accessibility and the evidence base. This is about academic judgement and institutional policy, so we work within your university's regulations.
Reviews
4.7
3 ratingsSample
- Femi O.Sample
Rigorous and balanced. The oral assessment rubric work was the highlight. Workload estimates for large cohorts are still a worry it can't solve.
- Lena K.Sample
Clear on trade offs instead of selling one model. Our redesigned module now has a short viva and AI acknowledged drafts.
- Ingrid S.Sample
Testing my own essay question with an AI assistant in lesson one was sobering and useful. The programme level view got our board unstuck.
About the teacher
Lecturer and learning designer for universities, workplace training and coaching practices
9 tutors 410 lessons taught Sample
My background is university teaching followed by learning design work, building courses with subject experts and turning them into something people can actually learn from. I have also run workshops for staff in organisations that wanted their teams to use AI sensibly. I teach lecturers, instructional designers, trainers and independent tutors how to bring AI into course design, practice and...
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