'Urgent need' for more AI literacy in higher education
16 Oct 2025
A new report from the Higher Education Policy Institute calls for immediate action to raise AI literacy for both staff and students across UK universities. The report spans institutional strategy, teaching and assessment, research, and professional services-urging leaders to move from awareness to structured capability.
Move beyond awareness to action
Wendy Hall and Giles Carden argue that simply acknowledging AI exists is not enough. Universities need a plan: clear principles, defined use cases, and continuous skills development to stay relevant and effective.
Build AI literacy for staff and students
Kate Borthwick highlights how AI challenges established approaches to delivery and assessment. Training cannot be optional-both staff and students need ongoing support to use AI well and maintain academic standards.
- Set a minimum AI literacy baseline for all staff and students (e.g., prompts, verification, citation, bias, IP, data privacy).
- Update assessment design: more authentic tasks, oral defenses, process logs, and tool-disclosure policies.
- Publish faculty-specific guidance on acceptable use, with examples by discipline.
- Run termly micro-learning: 60-90 minute sessions, with sandbox time and office hours.
- Create peer-led communities of practice and showcase practical use cases.
Address the new digital divides
The report warns of emerging divides in GenAI use by gender, wealth, and discipline. Equity needs to be designed in from day one.
- Provide institution-wide access to core AI tools and hardware support for students who need it.
- Offer targeted training and mentoring for underrepresented groups and disciplines with lower adoption.
- Embed accessibility and academic integrity in all AI guidance; monitor usage data for gaps.
Research: capability gaps and new support models
A chapter authored by ChatGPT notes wide variation in researchers' ability to use AI responsibly and effectively. Without focused investment, capability gaps will widen between and within institutions.
- Stand up an AI advisory function in research offices to support methods, reproducibility, and compliance.
- Integrate AI training into doctoral programmes, staff development, and research methods curricula.
- Adopt disclosure norms for AI use in proposals and publications; document data provenance and limits.
- Provide safe environments for code, data, and tool use aligned with security and ethics requirements.
Jobs and professional services: do more with fewer roles
Ant Bagshaw warns that generative AI will likely reduce professional services headcount as finances tighten. Some roles remain essential, but automation pressure will grow.
- Run workforce planning now: map tasks to automate, redesign roles, and invest in reskilling.
- Prioritise high-volume processes (admissions queries, scheduling, procurement, finance ops) for pilots.
- Track time saved and reinvest gains into student support, advising, and frontline teaching.
- Engage unions and staff early; set clear guardrails and quality checks for AI-assisted workflows.
Immediate actions for university leaders
- Appoint an executive sponsor and cross-functional AI steering group.
- Publish university-wide AI principles, acceptable-use guidance, and tool-disclosure requirements.
- Launch short AI literacy bootcamps for faculty, professional services, and students within 90 days.
- Refresh assessment policies to protect integrity and reduce over-reliance on essays.
- Centralise procurement for AI tools; address privacy, data residency, and accessibility.
- Fund equity measures: device loans, licenses, targeted training, and assistive tech.
- Establish a research AI advisory team and include AI modules in doctoral training.
- Create a change impact dashboard: adoption, outcomes, integrity incidents, and cost savings.
- Maintain an AI risk register covering bias, errors, IP, and security-review quarterly.
Useful resources
- Higher Education Policy Institute - reports and briefings on AI and policy.
- Jisc guidance on generative AI in education - practical frameworks and case studies.
Build capability at pace
If your institution lacks structured training, start now. Curate short, role-based pathways and measure adoption and outcomes.
- Role-based AI course pathways - map skills to responsibilities across academic and professional services.
- Latest AI courses - up-to-date options for quick faculty and staff upskilling.
The opportunity is clear: invest in skills, protect integrity, and focus on equity. Institutions that act with intent will deliver better learning, better research, and better use of scarce resources.
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