June 2026

AI and the learning alignment problem11 June 2026An invited keynote webinar for the Global Research Nursing Education Network, whose members work across the US, Canada, and Africa. The talk names a learning alignment problem in education that is structurally identical to the value alignment problem in AI safety: systems optimise the measurable proxy rather than the thing the proxy stands for, and AI has made the gap between the two impossible to ignore. It works through what nursing formation actually requires, why more accurate detection cannot help, and what shifts when educators design for the conditions of cognitive struggle rather than for the artefact.

The verification trap11 June 2026Institutions everywhere now insist on the same rule — use AI, but always verify its output. Yet verifying AI output is hard, slow, and often inconclusive, even in domains where you're competent to judge it. This post pushes that advice to its limit to see what breaks, and finds that as models improve, the verification mandate quietly trains the opposite of the scepticism it intends.
Building a CPD app with Claude Code8 June 2026Most CPD tools are built for compliance, not development. I wanted something that started from the gap; what am I missing, and what would close it? I couldn't find the tool I needed so I built one called Path instead. It describes what I think professional development should look like, and what two weekends with Claude Code produced.
What software engineers can teach us about AI in doctoral research1 June 2026PhD students are using AI across their doctoral work, but current policies focus on permission rather than specification. Drawing on how software engineers solved a structurally identical problem, this post introduces the research harness: a seven-part operating context that makes AI contributions visible, traceable, and supervisable. The harness addresses drift, offloaded thinking, and attribution failures — not by restricting AI use, but by specifying what the agent can and cannot do within the work.

A theoretical framework for integrating AI into HPE: a one-page guide7 June 2026A one-page reference guide for health professions educators. Condenses a theoretically grounded framework into six design principles for integrating AI in ways that support — rather than undermine — the conditions under which professional learning occurs. Each principle includes a brief description and an apply-by prompt for immediate use in learning activity design, curriculum review, or institutional policy.