About this guide
- Author: Michael Rowe (ORCID; mrowe@lincoln.ac.uk)
- Affiliation: University of Lincoln
- Created: June 7, 2026
- Format: One-page reference guide (PDF)
- License: Creative Commons Attribution 4.0 International
Existing frameworks for AI in education focus on what practitioners need to know about AI — competencies, literacy, tool use. The question they leave open is what conditions support professional learning with AI. This guide distils a theoretically grounded answer into six design principles, each derived from a point of convergence across four learning theories. The principles describe what effective learning environments look like; they apply whether or not AI is present, and they become urgent because AI can either support or undermine each of them depending on how it is integrated.
It is a practical companion to the full essay, condensed into a single page for use at the point of design — when planning an AI-supported learning activity, reviewing a curriculum, or auditing an institutional policy on AI.
Preview
How to use it
Use the six principles as design constraints, not a checklist. At the level of a learning activity, ask which principle the AI interaction supports and which it risks undermining. At the level of curriculum and assessment, ask whether the assessment measures artefact production or the reasoning behind it — and whether AI simply makes that distinction more visible, or more urgent. At the level of institutional policy, ask whether the AI policy is grounded in how professional learning works, or in what is easy to audit.
Each principle includes an apply by prompt — a one-line starting point for translating the principle into a concrete design decision.
The guide is derived from the full essay: A theoretical framework for integrating AI into health professions education.