Draft an institutional generative AI use policy that clearly separates permitted assistance from academic misconduct.
Digital Transformation & Artificial Intelligence
Generative AI Policy, Assessment Design and Academic Integrity in Universities
Design university generative AI policy and redesign assessment so academic integrity holds up when students have ready access to AI writing tools.
🗓️Duration5 training days
📚Content4 modules · 8 sessions
🎓On completionAccredited attendance certificate
About the programme
Course Overview
Generative AI writing tools are now widely available to students, and universities that rely on take-home essays and unsupervised written assignments as their primary assessment method face a genuine integrity problem that detection software alone cannot solve, since detection tools produce unreliable results and are contested as evidence. This course helps academic leaders and programme teams build a coherent institutional response: a clear generative AI use policy that distinguishes permitted assistance from misconduct, assessment redesign that reduces reliance on formats AI tools complete easily, and a misconduct investigation process built on defensible evidence rather than a single detection score. Sessions work through real assessment redesign exercises across disciplines, policy drafting that survives challenge at an academic appeals panel, and staff development that helps academics use generative AI productively in their own teaching and research while managing student use. Participants leave with a draft institutional policy, redesigned assessments for at least one of their own courses, and an investigation process that treats students fairly while protecting the value of the qualification.
Expected Learning Outcomes
Redesign assessments to reduce reliance on formats that generative AI tools can complete without genuine student learning.
Evaluate the reliability limits of AI detection tools and avoid relying on a single score as proof of misconduct.
Build a misconduct investigation process based on defensible evidence that would withstand an academic appeal.
Design authentic assessment formats, such as oral defences and in-class tasks, appropriate to specific disciplines.
Train academic staff to communicate AI use expectations consistently across a programme or faculty.
Support staff in using generative AI productively in their own teaching, feedback and research practice.
Who Should Attend
Academic integrity officers responsible for institutional policy and misconduct case handling.
Programme leaders and course convenors redesigning assessment for their own modules.
Learning and teaching development staff supporting academic staff through the policy change.
Registrars and academic appeals panel members who adjudicate misconduct cases.
Faculty deans setting institution-wide direction on generative AI in teaching and assessment.
Quality assurance staff responsible for validating that redesigned assessments meet learning outcomes.
Course Modules
Select any module to see its sessions and points.
01Establishing Institutional Generative AI Policy
2 sessions · 8 points
Session 1Defining Permitted Use and Misconduct Boundaries
- Distinguish acceptable AI-assisted activities, such as brainstorming or grammar checking, from submission of AI-generated work as one's own.
- Draft policy language specific enough to guide staff and students without becoming unworkable across every discipline.
- Address how policy varies by assessment type, from open-book essays to closed supervised examinations.
- Coordinate policy wording with national qualification frameworks and sector guidance on academic integrity.
Session 2Communicating Policy and Setting Staff and Student Expectations
- Communicate AI use policy to students in plain language at the start of every module, not buried in a handbook.
- Require course-level disclosure of what AI assistance, if any, is permitted for each specific assessment.
- Train academic staff to apply policy consistently rather than setting informal, conflicting expectations across modules.
- Establish a feedback channel for staff and students to flag ambiguous cases as policy is applied in practice.
02Redesigning Assessment for the Generative AI Era
2 sessions · 8 points
Session 1Identifying Vulnerable Assessment Formats
- Audit existing assessments to identify formats, such as generic take-home essays, most easily completed by generative AI.
- Distinguish assessments testing recall or standard argument structure from those testing genuine analysis or original work.
- Prioritise redesign effort on high-stakes assessments where integrity risk has the greatest consequence.
- Map which learning outcomes are actually being assessed before deciding how to redesign a task.
Session 2Designing Authentic and AI-Resistant Assessment Formats
- Design oral defences, vivas or presentations that require students to explain and justify their own submitted work.
- Build in-class, supervised or process-based assessment components that capture work as it develops, not only the final product.
- Design assessments around personalised, current or local data that generic AI-generated content cannot easily replicate.
- Balance authentic assessment redesign against staff workload and marking capacity constraints.
03Detection, Evidence and Investigation
2 sessions · 8 points
Session 1Understanding the Limits of AI Detection Tools
- Explain why AI detection tools produce false positives and false negatives that make a single score unreliable evidence.
- Compare detection tool outputs against other evidence, such as inconsistent writing style or an implausible submission timeline.
- Avoid policies that treat a detection score alone as proof of misconduct in a formal hearing.
- Communicate detection tool limitations honestly to staff so expectations about their reliability are realistic.
Session 2Building a Fair and Defensible Investigation Process
- Design an investigation process that combines multiple evidence sources before a misconduct allegation proceeds.
- Give students a fair opportunity to explain their working process and evidence their own contribution.
- Train panel members to weigh evidence consistently across cases to avoid disparate treatment of students.
- Document investigation outcomes and reasoning in a form that withstands challenge at an academic appeal.
04Supporting Staff and Sustaining the Programme
2 sessions · 8 points
Session 1Helping Academic Staff Use Generative AI Productively
- Train staff to use generative AI for drafting feedback, generating practice questions and preparing teaching materials.
- Address staff concerns about job security or diminished teaching value when introducing AI tools into their workflow.
- Share discipline-specific examples of productive staff AI use to build confidence and consistent practice.
- Establish guidance on what staff must disclose when using AI tools in their own marking or feedback.
Session 2Monitoring, Reviewing and Evolving the Institutional Approach
- Track misconduct case volumes and outcomes over time to assess whether policy and assessment changes are working.
- Review assessment redesigns against student outcomes and staff workload after at least one full delivery cycle.
- Update policy as generative AI capability and sector guidance evolve rather than treating it as a one-off exercise.
- Share lessons and effective assessment redesigns across departments to accelerate institution-wide adoption.
What the participant receives
4 course modules
A structured syllabus
8 training sessions
across 5 days
32 detailed points
Applied, detailed content
Accredited attendance certificate
On completing the programme
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