AI in higher education: from policy anxiety to practical pilots

Who this is for

Deans, heads of school, programme leaders, and research coordinators who need AI policy, governance, and disciplined pilots that respect disciplinary differences.

Typical roles: Faculty leadership responsible for assessment policies, research strategy, or operational efficiency. Also relevant for professional services teams supporting teaching and research.

Where institutions get stuck

• Policies copied from generic templates that do not fit disciplines.
• Staff and students already using AI, but without clear guidance or disclosure.
• Anxiety around assessment integrity, IP, and research reproducibility.
• Debate without data: few structured pilots to learn from.

Policy and governance pillars

• Assessment: what is permitted, what must be disclosed, and how to verify.
• Teaching/learning: discipline-aware guidance and examples, not blanket bans.
• Research: data handling, provenance, reproducibility, and IP ownership.
• Staff use: clarity on administrative assistance vs. substantive academic work.
• Procurement: minimum standards for vendors and storage of institutional data.

A practical path forward

1. Map current use: how staff and students already lean on AI tools.
2. Define guardrails for assessment, supervision, and research data handling.
3. Choose 1 to 2 pilots (e.g., feedback drafting, literature synthesis, admin workflows).
4. Measure outcomes (time saved, quality, satisfaction) and refine policy with evidence.

Assessment and academic integrity

• Set discipline-specific guidance; avoid one-size-fits-all statements.
• Define allowed vs. disallowed activities and clear disclosure formats.
• Use rubrics that reward process and originality; reduce reliance on surface features.
• Train staff on reviewing AI-assisted work and handling suspected misuse.

Research and data considerations

• Decide what data can be processed outside institutional systems vs. in secure environments.
• Keep prompt/version history for reproducibility and peer review.
• Track citations/sources when using AI for literature synthesis.
• Clarify IP ownership and authorship where AI contributes to outputs.

Example pilots

• Assessment support: structured feedback drafting with clear disclosure rules.
• Research support: reproducible prompt libraries with versioning and citations.
• Academic admin: automating minutes, action tracking, and policy communications.
• Student services: triaged FAQs with human-in-the-loop escalation.

Change and capability building

• Deliver short, role-specific workshops: teaching staff, researchers, admin teams.
• Publish quick-start guides with examples per discipline.
• Offer office hours to handle edge cases and refine guidelines with feedback.
• Create a light approvals process for new tools and pilots.

What we deliver

• Discipline-aware AI guidelines and disclosures staff can work with.
• Pilot playbooks with clear roles, data rules, and success metrics.
• Workshops tailored to academic staff and leadership concerns.
• Recommendations for safe tooling, procurement, and roll-out sequencing.

Measuring impact

• Time saved on feedback and admin tasks.
• Quality and consistency of feedback (student surveys, moderation outcomes).
• Staff confidence using AI within policy boundaries.
• Reduced incidents of policy violations or unclear disclosures.

How to engage

We begin with a short, paid Discovery & Governance Sprint to surface where AI can help, where it should be limited, and what evidence you need to move policy forward confidently.