- Why a research AI policy is a different document from a teaching AI policy
- How Oxford, Cambridge, Edinburgh, Durham and Manchester compare
- Common questions on university AI policy
- Where the coverage still has gaps
- What this means for research offices
Most coverage of university AI policy in the UK is really about teaching and assessment: can a student use ChatGPT on an essay, does a lecturer need to declare AI-marked feedback. Far less attention goes to the separate question research offices actually have to answer — what generative AI use is permitted in grant applications, literature reviews, data analysis, manuscript drafting and peer review. This article sets out what five Russell Group institutions — Oxford, Cambridge, Edinburgh, Durham and Manchester — have published specifically for research, and where the gaps between “policy exists” and “policy is usable” still sit.
Why a research AI policy is a different document from a teaching AI policy
UK universities largely converged on shared teaching principles early. In January 2024 the Russell Group published five principles on generative AI in education — covering AI literacy, staff support, curriculum adaptation, academic integrity and shared best practice — and all five institutions in this review have adopted or referenced them.
Research governance is a separate exercise. It has to address funder compliance, data protection for unpublished results, intellectual property, and authorship — questions the teaching principles do not touch. Only some institutions have built a dedicated instrument for this.
- Grant-application drafting and hypothesis generation
- Literature review and translation of non-English sources
- Code generation and synthetic data creation
- Manuscript preparation, editing and peer review
- Data protection for unpublished or sensitive research material
How Oxford, Cambridge, Edinburgh, Durham and Manchester compare
Oxford has the most explicit stand-alone instrument of the five. Its Policy for using generative AI in research, maintained by the Research Practice Sub-Committee, lists permitted uses — interpreting data and texts, literature review and translation, identifying research gaps, generating hypotheses, and producing code or synthetic data — and requires researchers to declare the tool name, version, publisher and URL in any resulting output.
Cambridge’s research-facing AI principles sit largely with Cambridge University Press & Assessment rather than a single central research office document, alongside separate Information Compliance guidance for administrative tasks. The Press principles are unambiguous: AI and LLM tools cannot be listed as an author on any scholarly work, because authorship requires an accountability an AI system cannot hold. Faculty-level practice still varies — some departments, such as History and Philosophy of Science, take a materially more restrictive line than the centre.
Edinburgh has no single, university-wide research AI policy equivalent to Oxford’s. Guidance instead sits with individual schools and institutes — the Institute for Advanced Studies in the Humanities on fellowship-proposal drafting, the School of Philosophy, Psychology and Language Sciences on privacy and copyright — layered on top of ELM (Edinburgh access to Language Models), a centrally hosted gateway intended to keep sensitive research data out of third-party AI systems.
Durham’s most developed AI documentation is framed around academic misconduct and assessment rather than research practice specifically, though a “Guidance on Generative AI in Research” document exists and is reviewed annually. Note that the Common Awards Partnership — cited by a May 2026 HEPI study as one of only four national exemplars for trust-based AI framing — is a teaching-validation partnership between Durham and around thirty theological colleges, not Durham’s central research policy; the two should not be conflated.
Manchester’s AI Hub guidance is the broadest in scope of the five, explicitly spanning research, teaching, operations and procurement under five core principles, and it is the only one of the five reviewed here to fold environmental and energy-impact considerations into its AI governance rather than treating them as a separate sustainability workstream.
| Institution | Dedicated research AI instrument | Coverage | Distinctive feature |
|---|---|---|---|
| Oxford | Policy for using generative AI in research (Research Practice Sub-Committee) | University-wide | Mandates declaring tool name, version, publisher and URL |
| Cambridge | CUP & Assessment AI research ethics principles + Information Compliance guidance | Central principles, faculty-level variation | Explicit authorship ban; named tool allow-list (Copilot, Gemini, NotebookLM) |
| Edinburgh | No single university-wide document; school/institute-level guidance | Fragmented by school | ELM — centrally hosted, data-protection-compliant AI gateway |
| Durham | Guidance on Generative AI in Research (annually reviewed) | University-wide guidance, departmental variance | Common Awards Partnership named a national HEPI exemplar (teaching context) |
| Manchester | AI Hub guidelines (five core principles) | Cross-functional: research, teaching, operations, procurement | Embeds environmental/energy-impact review into AI governance |
Common questions on university AI policy
Does Oxford University have a separate AI policy for research?
Yes. Oxford’s Research Practice Sub-Committee maintains a dedicated Policy for using generative AI in research, distinct from teaching and assessment rules. It lists permitted uses — literature review, hypothesis generation, code and synthetic-data generation — and requires researchers to declare the tool name, version, publisher and URL in any resulting publication.
Can generative AI be listed as an author on a research paper?
No UK institution reviewed here grants AI authorship. Cambridge, via Cambridge University Press & Assessment, states explicitly that AI and LLM tools cannot appear as authors because authorship requires an accountability an AI tool cannot hold — a position consistent with long-standing ICMJE and COPE authorship criteria used across UK research offices.
Do all UK universities have a public AI policy?
No. A May 2026 HEPI study of 96 institutions found that 41% of UK degree-awarding institutions have no publicly accessible AI policy at all — some sit behind staff-only login walls, some return broken links. Coverage of research-specific, as opposed to teaching, AI use is patchier still.
What tools are UK universities recommending for AI-assisted research?
Rather than banning generative AI outright, several institutions steer researchers toward vetted, university-licensed tools. Cambridge names Microsoft Copilot, Gemini and NotebookLM for administrative tasks; Edinburgh routes staff and students through ELM, a centrally hosted gateway built to keep sensitive research data out of third-party AI systems.
Where the coverage still has gaps
The HEPI Policy Note also found that most of the 96 publicly accessible policies it scored as “education-dominant” by keyword count were, on close reading of a smaller sample, actually detection-and-discipline frameworks dressed in educational language — with a policy’s location (inside an academic-misconduct framework versus a learning-and-teaching framework) predicting its real function more reliably than its wording. That distinction matters directly for research offices: a policy hosted under misconduct procedures tends to police disclosure after the fact, while a policy hosted under research practice guidance — as at Oxford — tends to define acceptable use up front.
None of the five institutions here has published a shared, sector-wide position on how AI-tool involvement should be logged against individual contributions to a paper. CASRAI originated the CRediT contributor role taxonomy in 2014; the standard is now stewarded by NISO as ANSI/NISO Z39.104-2022. As institutions refine AI-declaration requirements, a role-based framework already designed to record who did what on a paper is one obvious place to look for a shared vocabulary, rather than each research office inventing its own declaration format from scratch.
What this means for research offices
For institutions still drafting or revising research-specific AI guidance, three patterns from this review are worth acting on directly: separate the research document from the teaching document rather than relying on assessment rules to cover research use by implication; specify a declaration format (tool, version, publisher, date accessed) rather than a general disclosure expectation, since Oxford’s precedent shows this is enforceable; and name approved tools explicitly, because Cambridge’s and Edinburgh’s allow-list approach reduces the shadow-IT risk of researchers defaulting to unvetted consumer AI products for sensitive material.
Research administrators working across international collaborations should also expect continued divergence rather than convergence in the near term: with 41% of UK institutions still lacking a public policy and the remainder split between misconduct-framed and practice-framed approaches, funders and publishers are likely to keep filling the gap with their own AI-declaration requirements ahead of any single UK sector standard. Research offices that document their own position clearly now — and locate it in research practice guidance rather than misconduct procedure — will be better placed to respond when that standardisation eventually arrives.