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A workable university generative-AI policy does four things at once: sets institution-wide safeguards, leaves room for course and discipline-specific judgment, protects research data and integrity, and gives people practical support. Start by mapping existing regulations and assigning authority; then define scope, translate principles into duties, publish clear teaching and assessment rules, create a separate research-control framework, and establish ownership, training and scheduled review.
UNESCO’s human-centred guidance is a useful starting point. In its foreword, UNESCO Assistant Director-General for Education Stefania Giannini writes: “AI must not usurp human intelligence.” That principle should become operational requirements for human decision-making, privacy, equity, transparency and accountability—not remain a slogan.
1. Define what the policy is for—and what it is not
Write a short purpose statement before drafting rules. It should explain how generative artificial intelligence (GenAI) may support teaching, learning, assessment, research and relevant administrative work while preserving human judgment, academic standards, privacy, security and fair treatment.
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UNESCO’s Guidance for generative AI in education and research, published 7 September 2023 and updated on its page 16 January 2026, advocates human-centred use, privacy protection, ethical validation, pedagogical design and coherent frameworks for education and research. It is international guidance, not a substitute for the university’s law, contracts or regulations.
The OECD’s Policies supporting responsible and systematic GenAI adoption in higher education (Education Spotlights No. 23, 17 July 2026) identifies five system-level response areas: government or national guidance; compliance and procurement approaches; GenAI competencies; evidence collection and pilot evaluation; and specialised higher-education tools. These are planning categories, not a statistic about how many universities have adopted a policy.
2. Establish authority, scope and the policy hierarchy
Map the rules that already bind the university
Before writing substantive clauses, identify the body that can approve the policy and the offices that already own related rules. At minimum, map:
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- academic-integrity and misconduct procedures;
- assessment regulations and examination codes;
- research-integrity and responsible-conduct procedures;
- privacy and data-protection requirements;
- information-security and cybersecurity standards;
- intellectual-property, copyright and licensing rules;
- research-ethics and human-participant review;
- records-management, export-control and contract requirements;
- procurement and vendor-risk processes; and
- accessibility, equality and non-discrimination duties.
State which document prevails if provisions conflict. The University of Liverpool, for example, says its GenAI teaching guidance operates alongside Appendix L of its Code of Practice on Assessment, with the assessment code taking precedence in regulatory terms. That relationship should be explicit in your own policy rather than left to interpretation.
Specify who and what is covered
Define “generative AI” in plain language as systems that produce or transform text, code, images, audio, video or other content from user instructions or data. Explain whether the definition includes embedded features in institutional software, automated translation, coding assistants, transcription, retrieval-augmented systems and locally hosted models.
Name covered users: students, instructors, teaching assistants, supervisors, researchers, professional staff, contractors and visitors using university systems. List covered contexts separately: learning, teaching preparation, assessment design and marking, research design and conduct, publication, student support and administration. If a faculty document excludes researchers without teaching duties, as the University of Geneva Faculty of Science recommendations do, say so; otherwise readers will assume the rule is universal.
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Resolve local-versus-institutional rules
Include a clause such as: “A course, programme, laboratory or research project may impose stricter requirements where needed to preserve the learning outcome, comply with a funder or publisher, protect data, or meet professional standards. Local rules must be published before the relevant activity and may not remove protections required by university policy or law.” Add a conflict route: the responsible dean, research office or policy owner decides which interpretation applies while formal advice is obtained.
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3. Turn principles into duties people can follow
Begin with a short set of principles, then attach an accountable actor and a minimum action to each one. Avoid a stand-alone instruction to “use AI responsibly”; it gives neither a student nor an investigator a testable standard.
| Principle | Operational requirement | Primary accountable actors |
|---|---|---|
| Human agency | A named person remains responsible for decisions, submitted work, grades, interpretations and research conclusions. | All users; instructors and principal investigators for their decisions |
| Academic integrity | Do not present generated material as personal work, fabricate evidence or outsource a skill that an assessment is designed to measure. | Students, instructors, supervisors |
| Privacy and confidentiality | Classify data before entering it into a tool; use only approved environments for protected information. | All users; privacy, security and procurement offices |
| Equity and accessibility | Provide an equivalent way to meet learning or work requirements when access, disability, cost or language creates a barrier. | Instructors, programme leaders, student-support services |
| Transparency | Record and disclose material tool use, limitations, checking and mitigation in the place appropriate to the activity. | Students, researchers, authors and staff |
| Reliability and rigour | Verify claims, citations, calculations, code and outputs against authoritative evidence before relying on them. | Every user; supervisors and reviewers |
Use definitions and examples in an accompanying guide, but keep enforceable obligations in the approved policy. This separation lets the university update examples without reopening the entire instrument.
4. Write clear teaching and assessment rules
Use a permission model that matches the task
Do not classify an entire degree as “AI allowed” or “AI banned.” Classify uses by activity and explain the reason. A three-level model is easy for students and instructors to apply:
| Level | Typical uses | Required controls |
|---|---|---|
| Allowed with acknowledgement | Brainstorming, language practice, translation followed by checking, self-testing, coding hints, summarising a student’s own notes, or generating illustrative examples. | Student remains responsible; disclose the tool and material contribution when the course requires it; verify accuracy and citations. |
| Restricted or instructor-approved | Drafting substantial prose, generating code used in assessed work, analysing data, creating images, or using AI during a take-home examination. | Written permission, defined boundaries, an approved tool or environment, retained prompts or logs where required, and a method to demonstrate individual mastery. |
| Prohibited for the task | Submitting generated work as one’s own, supplying examination questions to a public tool, or using GenAI where the assessment measures unaided performance. | Explain the learning or security rationale and apply the university’s fair academic-integrity process if a concern arises. |
The University of Geneva Faculty of Science recommendations (version December 2025) illustrate permitted student activities such as discussion, self-testing, checked translation, summarisation, coding assistance and illustrations. They prohibit passing generated work off as one’s own, providing exam questions, and entering sensitive or confidential data or unpublished research results.
Require timely, written communication
Instructors should put the rule in the syllabus, assessment brief and learning platform before students begin the work. Each notice should state:
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- whether GenAI is allowed, restricted or prohibited for this specific task;
- which functions are permitted, such as idea generation but not drafting;
- what must be disclosed or cited, in what format, and whether prompts or output versions must be retained;
- which data may not be uploaded;
- how a student can ask for an accommodation or clarification; and
- how suspected violations will be handled under existing procedures.
A concise syllabus clause might read: “For this assessment, you may use GenAI to brainstorm and to check grammar. You may not generate the analysis or final answer. In an appendix, name each tool, date of use, purpose and the parts you changed or verified. Do not upload personal, confidential or unpublished information.” Adapt the wording to the institution’s assessment code and the actual learning outcomes.
Design assessments that preserve the intended skill
State the capability being measured and make the evidence visible. Options include staged drafts, process notes, source annotations, oral explanations, supervised tasks, version histories, locally relevant data or reflections on methodological choices. Geneva specifically recommends oral assessment as one way to check mastery. It is a design choice, not proof that every course must add an oral examination.
Do not ask an AI detector to decide misconduct. Geneva notes that no reliable method for identifying GAI use has been established. Use the student’s disclosed process, inconsistencies in sources or drafts, comparison with the stated task rules and a fair institutional procedure. Never treat a detector score as conclusive evidence.
Define acceptable staff use
Teaching staff may use GenAI for activities such as brainstorming lesson ideas, drafting lesson plans or supplementary materials, summarising feedback, preparing multiple explanations, drafting communications for review, creating low-stakes test questions, refining rubrics and generating visual or audio/video materials. Liverpool lists these as educational use cases while stressing human review.
Require an instructor to check factual accuracy, accessibility, bias, copyright and alignment with the learning outcome before publication. AI must not generate grades or final assessment decisions. Liverpool identifies copying unreviewed AI output into assessment briefs, rubrics or feedback as unacceptable, as well as uploading student work, personal data or confidential information to public platforms without the required data-protection process.
5. Give research its own control framework
Teaching rules cannot cover research risks such as unpublished results, participant data, intellectual property, funder conditions, authorship and reproducibility. Create a separate research section that applies from project design through storage, analysis, publication and archiving.
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Classify information before using a tool
| Information type | Default rule | Decision or evidence required |
|---|---|---|
| Public, non-sensitive material | May be used in an approved tool after checking licence and output risks. | Document the tool, purpose and verification. |
| Personal, confidential, contractual or security-sensitive data | Do not upload to a public service. Use only an environment whose protections and terms have been approved. | Privacy/security review and any required consent or ethics approval. |
| Unpublished results, manuscripts, peer-review material or protected intellectual property | Do not enter unless confidentiality, ownership and contractual permissions are assured. | Written approval from the project owner, research office or other designated authority. |
| Restricted or regulated research data | Follow the stricter project, funder, law, ethics and security requirements. | Documented data-management and ethics decisions. |
Oxford’s research policy says that uploading personal data is processing, that sensitive or confidential information should be uploaded only where confidentiality can be guaranteed, and that research use requires ethics approval where applicable. Those are Oxford’s arrangements; your policy must point to your own privacy and ethics processes.
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Generated text can contain fabricated citations, plausible errors and hidden assumptions. Generated code can introduce insecure or unlicensed components. Require researchers to check claims, references, calculations, code, translations and images against authoritative sources and to retain enough evidence to reproduce the work.
Specify what disclosure means. Depending on the discipline and publisher, a methods section, acknowledgements, data-management record or project log may identify the tool and version, dates or period of use, purpose, material prompts or transformations, human checks, known limitations and mitigations. Oxford recommends disclosing tool interactions, limitations and mitigations in an appropriate section because cloud models change over time and can undermine reproducibility and transparency.
Address authorship, attribution and publication
State that people—not software—hold responsibility for interpretation, originality, authorship declarations and compliance with publisher or funder rules. Require permission before processing a collaborator’s or participant’s material, and prohibit unattributed reuse that violates copyright or licence terms. Link the policy to research-integrity procedures so suspected fabrication, plagiarism, undisclosed conflicts or confidentiality breaches follow an established investigation route.
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A policy that only prohibits conduct will be bypassed or applied inconsistently. Name the services that make compliance possible.
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Provide training by role
- Students: practical exercises on permitted use, disclosure, citation checking, bias, hallucinations, privacy and assessment-specific rules.
- Instructors: assessment design, accessible alternatives, feedback review, oral checks and handling questions or suspected breaches.
- Researchers and supervisors: data classification, ethics, reproducibility, tool limitations, authorship and disclosure.
- Professional staff: records, procurement, accessibility, security and confidential-information handling.
Offer examples, not just a slide deck: model syllabus statements, a research disclosure example, a data-classification decision tree, approved-tool information and a contact route for urgent questions.
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Define procurement and tool approval
Assign an office to evaluate vendors and embedded features for data retention, model training, jurisdiction, access controls, deletion, accessibility, intellectual-property terms, security and service continuity. Tell users how to request a new tool and what to do when a platform changes its terms. Do not imply that a consumer service is safe merely because it is popular or free.
7. Assign ownership, monitoring and review
Name a policy owner and a cross-functional group with authority to coordinate academic affairs, teaching and learning, research, privacy, security, accessibility, libraries, students and legal counsel. Give that group responsibility for questions, training, incident trends, approved-tool information and proposed amendments.
Separate formal policy changes from administrative updates. Changes to rights, duties, sanctions or the regulatory hierarchy should follow the university’s approval route. FAQs, examples, contact details and non-substantive technical guidance can usually be updated by the policy owner under delegated authority.
Set a review date and an interim trigger for major changes in law, contracts, model capabilities, security risk or assessment practice. Oxford reports a policy advisory group that reviews its policy and FAQs termly; present that cadence as an institutional choice, not a universal requirement. Record decisions and publish the current version, effective date and change log.
8. An eight-step drafting and approval workflow
- Inventory rules and risks. Collect the current assessment code, integrity procedures, privacy and security standards, ethics requirements, contracts and procurement controls.
- Confirm authority. Identify the approving body, policy owner, escalation officers and the document that prevails in a conflict.
- Define scope and terms. List users, activities, AI functions, embedded tools and exclusions in language a student can understand.
- Write principles and actor duties. Convert human agency, equity, privacy, integrity, transparency and rigour into observable actions for students, instructors, supervisors, researchers, administrators and support offices.
- Build the teaching and assessment section. Add permission levels, written-notice requirements, disclosure examples, assessment-design options and a fair route for questions and suspected violations.
- Build the research section. Add data classification, approved environments, human verification, disclosure, reproducibility, ethics and incident escalation.
- Attach implementation resources. Publish syllabus language, disclosure templates, tool-evaluation criteria, training, accessibility guidance and contact points.
- Consult, approve and maintain. Test the draft with students, instructors and researchers; obtain privacy, security, ethics and legal review; approve it through the correct governance route; publish a versioned policy and schedule its review.
9. What institutional examples show—and what they do not prove
| Example | Useful design feature | Limit on applying it elsewhere |
|---|---|---|
| UNESCO guidance (2023; page updated 2026) | Human-centred, privacy-protective and ethical framework for education and research. | International guidance; it does not replace local law or university regulations. |
| OECD synthesis (17 July 2026) | Five response areas: guidance, compliance/procurement, competencies, evidence and pilots, and specialised tools. | A policy framework, not an adoption-rate measurement. |
| University of Oxford research policy | Integrates GenAI with research conduct, data protection, IP, security, ethics, disclosure, training and termly advisory review. | Oxford’s governance, contracts and legal setting may differ from yours. |
| University of Liverpool teaching guidance (updated July 2026) | Clarifies educational use cases, requires review, and places the assessment code above guidance. | Its Appendix L and institutional procedures are not automatically transferable. |
| University of Geneva Faculty of Science recommendations (December 2025) | Shows how faculty-level permission, timely written course rules, disclosure and oral checks can work. | Applies to that faculty community and expressly does not cover every researcher. |
Compare examples on baseline-versus-local discretion, student permission and disclosure, research-data controls, named owners and support, regulatory relationships and review cadence. Do not rank a policy by how permissive it sounds; legal setting, academic regulations and research domains change the appropriate balance.
10. Pre-approval quality check
- Can a student tell, before starting, what is allowed for each assessment?
- Can an instructor explain why a restriction protects a stated learning outcome?
- Can a researcher determine whether data may enter a particular tool without guessing?
- Does every obligation have an accountable person and a support contact?
- Does the policy require human verification rather than detector scores?
- Are privacy, security, IP, ethics, procurement and assessment rules linked in the hierarchy?
- Are disclosure, record-keeping and reproducibility expectations proportionate to the activity?
- Is there a fair process for questions, accommodations, suspected breaches and appeals?
- Are formal amendments distinguished from updateable FAQs and examples?
- Has the university checked current laws, regulations, contracts, vendor terms and review dates before approval?
The Bottom Line
The strongest university GenAI policy is a governed framework: institution-wide safeguards and accountability at the centre, explicit course and research discretion at the edges, and enough training, approved support and review capacity to make the rules usable as the technology changes.
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