Three principles
The approach taken throughout this guide
AI is a collaborator, not evidence
Use AI for generation, critique, organisation and transformation. Use scholarly sources, data and expert judgement for evidence.
Verification is structural
Build checks into prompts, workflows and assessment design instead of treating verification as a final tidy-up.
Boundaries make AI useful
Clear expectations about permitted use, disclosure and data protection are what turn AI from a risk into a working tool.
Explore the guide
Where to go next
Getting started
Strategy and integration
Why AI belongs in higher education, what changes for pedagogy and assessment, and how to integrate it well.
ReadAI literacy
Essential skills
The knowledge and critical habits academics need: capability, limitation, bias and verification.
ReadPrompting
Frameworks and templates
The 7-part prompt framework plus downloadable templates for feedback, tutoring and review.
ReadTeaching
Pedagogy in practice
Personalisation, assessment, materials, automation, analysis and accessibility — with clear boundaries.
ReadResearch
Overview and gateway
AI across the research lifecycle, and the route into the full verification-first research guide.
ReadPlatforms
The landscape
A researcher's map of the main generative AI platforms and what to check before trusting one.
ReadToolkit
Curated tools
NotebookLM workflows and a filtered set of academic tools for literature, content and analysis.
ReadEthics
Responsible practice
An evaluation checklist for tools and practices, and the wider societal questions AI raises.
ReadWriting Register Diagnostic Tool
Compare writing features with curated AI-style profiles. The comparison profiles are informed by a documented AI-output corpus and Bayesian modelling workflow — descriptive, not forensic.
Companion guides
Related AI literacy sites
Move between the staff, research, student and beginner-facing guides. Product features, pricing, model access and privacy settings change frequently, so check current provider documentation before relying on a platform for teaching, research or sensitive work.
