Generative AI has moved from novelty to an everyday study tool with remarkable speed. Universities have responded by producing policies, assessment guidance and academic integrity statements intended to tell students what they may and may not do. But a policy can exist without being genuinely useful. Students need to know where the boundary sits, what evidence they should keep, and whether legitimate forms of support are recognised. (Luo, 2024; Moorhouse et al., 2023).

We analysed student-facing generative AI guidance from 50 UK universities to examine how institutions are communicating these expectations. The picture is more encouraging than a simple narrative of bans and punishment would suggest. Most universities have moved towards conditional or supported use. At the same time, important gaps remain, particularly around disability, neurodivergence and the practical detail students need to apply rules to real assessments.

Blanket bans are now the exception

The most striking finding is that outright prohibition is relatively uncommon. Thirty of the 50 universities in our sample, or 60 per cent, adopted a conditional position. In these institutions, AI use is permitted in some circumstances, tasks or stages of work, subject to stated conditions. A further 14 universities, or 28 per cent, explicitly encouraged some AI-supported study within academic integrity boundaries.

Only three universities maintained a broad ban and another three took a more narrowly limited position. This matters because it suggests that the sector is moving beyond a simple question of whether students should use AI at all. The more difficult question is how they should use it, for which purposes, and with what evidence of their own learning.

Many policies now distinguish between AI used as support and AI used as outsourcing. Using a tool to generate revision questions, clarify a concept or comment on structure is treated differently from asking it to produce substantial assessed work that a student then presents as their own. That distinction is essential, but it only helps students when it is explained clearly.

Clarity is improving, but detail varies

Across the sample, 36 policies, or 72 per cent, were rated as highly clear. The strongest guidance tends to explain what is allowed, under what conditions, how AI use should be acknowledged and where students can seek further advice. This is an important development. More nuanced rules do not have to be confusing if they are communicated well.

However, a minority of policies still refer to AI only briefly or place it within wider misconduct lists without showing how the rules apply to common study situations. A student may understand that submitting AI-generated work as their own is unacceptable while still being unsure whether they can use AI for brainstorming, language support, coding assistance, planning or feedback on a draft.

This is why examples matter. Generic statements can establish principles, but task-level examples make the boundary usable. A policy that says students may use AI to generate ideas for a lab report but not to write a particular section is much easier to apply than a broad instruction to use AI responsibly. (Moore and Lookadoo, 2024).

The shift towards process evidence

Another important change is the growing emphasis on how work was produced rather than only what the final submission looks like. Thirty-three policies, or 66 per cent, explicitly expected some form of process evidence. Students might be asked to retain drafts, notes, prompts, logs or reflections, or to be able to explain how AI contributed to their work. A further eight policies referred to process more implicitly.

This can be a constructive development. If assessment is concerned with learning, then evidence of reasoning, revision and decision-making can be more informative than trying to infer everything from a finished product. Process evidence can also support more honest conversations about AI because it allows students to show where the tool was used and where their own judgement shaped the work. (Dawson et al., 2024).

The requirement needs to be practical, though. Telling students to keep evidence without specifying what counts as sufficient can create uncertainty. Process expectations should be designed into the assessment rather than added later as a defensive response to suspected misconduct.

Inclusion is the clearest gap

The weakest area in the policies we analysed was explicit attention to disability and neurodivergence. Only 22 of the 50 policies, or 44 per cent, mentioned disability, reasonable adjustments or accessibility in the AI context. Just two policies, or 4 per cent, explicitly named neurodivergent students.

This matters because generative AI can sit close to functions that some students use for access and support. It may help with planning, organisation, language, summarising or breaking a task into manageable steps. That does not mean every AI use should be permitted or that AI replaces formal reasonable adjustments. It does mean that a policy written only through the lens of misconduct can leave students unsure whether a legitimate support practice is acceptable. (Dwyer et al., 2023; Jisc, 2025).

Many universities already have strong separate commitments to inclusive assessment and reasonable adjustments. The problem is that those commitments are not consistently carried into AI guidance. When the two policy areas are disconnected, students are left to work out the relationship themselves. (Quality Assurance Agency for Higher Education, 2023; Waterfield and West, 2006).

What better student guidance looks like

Our findings point towards several practical principles for student-facing AI policy.

First, policies should distinguish clearly between support and outsourcing. Students need to know not simply that some AI use is allowed, but what kinds of assistance preserve the intended learning and what kinds replace it.

Second, expectations for acknowledgement and process evidence should be concrete. If prompts, drafts or reflections need to be retained, students should be told what to keep and why.

Third, examples should be specific enough to reflect real assessments. A student writing an essay, completing a coding exercise, producing a laboratory report or preparing a presentation will face different decisions.

Fourth, disability and neurodivergence should be addressed explicitly. Guidance should explain how AI-related support interacts with reasonable adjustments and how students can seek advice. Naming neurodivergent students where appropriate can also signal that their experiences were considered when the rules were written.

Fifth, sanctions should sit alongside guidance and support. Misconduct consequences need to be clear, but policy should also help students develop responsible practice rather than presenting AI only as a source of risk.

Finally, central policy needs to connect with programme and assessment-level instructions. Students should not have to reconcile one message from an institutional website with another in a module handbook and a third in an assessment brief.

The next stage is consistency

UK universities have already moved a considerable distance from the first wave of responses to generative AI. Most of the policies in our sample are conditional rather than prohibitive, many are clearly written, and process evidence is becoming a common part of assessment guidance.

The next stage is not simply to produce more policy. It is to make guidance more consistent, more specific and more inclusive. Students need rules they can apply to the assessment in front of them. They also need confidence that legitimate support needs have been considered rather than treated as an afterthought.

Generative AI policy is now part of the everyday assessment environment. Its quality will influence not only academic integrity but also trust, accessibility and students’ ability to make informed decisions about how they learn. Clear and inclusive guidance is therefore not a peripheral administrative task. It is part of good assessment design.

Sources and further reading

The sources below are those used in, or directly relevant to, the original article and the claims retained in this blog adaptation.

Dawson, P., Bearman, M., Dollinger, M. et al. (2024). Validity matters more than cheating. Assessment & Evaluation in Higher Education, 49(7), 1005–1016. https://doi.org/10.1080/02602938.2024.2386662

Dwyer, P., Mineo, E., Mifsud, K. et al. (2023). Building neurodiversity-inclusive postsecondary campuses. Recommendations for leaders in higher education. Autism in Adulthood, 5(1), 1–14. https://doi.org/10.1089/aut.2021.0042

Jisc (2025). Navigating the intersection of AI, accessibility and education in 2025. https://nationalcentreforai.jiscinvolve.org/wp/2025/03/06/navigating-the-intersection-of-ai-accessibility-and-education-in-2025/

Luo, J. (2024). A critical review of GenAI policies in higher education assessment. A call to reconsider the ‘originality’ of students’ work. Assessment & Evaluation in Higher Education, 49(5), 651–664. https://doi.org/10.1080/02602938.2024.2309963

Moore, S. and Lookadoo, K. (2024). Communicating clear guidance. Advice for generative AI policy development in higher education. Business and Professional Communication Quarterly, 87(4), 610–629. https://doi.org/10.1177/23294906241254786

Moorhouse, B. L., Li, W. C. and Walsh, S. (2023). Generative AI tools and assessment. Guidelines of the world’s top-ranking universities. Computers and Education: Open, 5, 100151. https://doi.org/10.1016/j.caeo.2023.100151

Quality Assurance Agency for Higher Education (2023). The improvement of student learning by linking inclusion/accessibility and academic integrity. https://www.qaa.ac.uk/membership/benefits-of-qaa-membership/collaborative-enhancement-projects/academic-integrity/the-improvement-of-student-learning-by-linking-inclusion-accessibility-and-academic-integrity

Waterfield, J. and West, B. (2006). Inclusive Assessment in Higher Education. A Resource for Change. University of Plymouth.