24 items with this tag.
Path is a CPD portfolio app that shows you what you're missing against the standard you're working towards, rather than only showing you what you've done. It began as a two-weekend prototype and has grown into a real, hosted web app that maps your claims and evidence against any framework — a promotion, a fellowship, a regulator's CPD standard — and shows you the single highest-value thing to do next. It's still a closed, invite-only alpha, and the waiting list is now open.
Most 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.
A 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.
Tim Fawns published a LinkedIn post setting out 17 points about student use of AI, drawing on his experience as a researcher and educator. The points push back on the framing that positions students as cheating or avoiding learning, and ask for a more sophisticated understanding of what's actually happening. I've formatted Tim's LinkedIn post as a one-page PDF for easier sharing.
One in seven people in the UK are using AI chatbots for health advice instead of seeing a GP. The institutional response has been to warn them off, but that response applies a standard it doesn't consistently apply to anything else in the system. This post argues that the risk comparison driving those warnings is systematically skewed, and that a more honest accounting points toward an entirely different kind of response.
An invited webinar for the Musculoskeletal Association of Chartered Physiotherapists. Explores what AI means for physiotherapy practice across the full information ecosystem of clinical work — from AI-assisted diagnosis and documentation to patient agency and the therapeutic relationship. The central concept is context sovereignty: AI systems work from professional context, and controlling that context is both the distinctive human contribution and the most practical skill for the AI age. The session covers the evidence for AI performance in clinical contexts, strategies for maintaining professional agency, how to actively support patients in using AI well, and how the therapeutic relationship is changing as practitioners and patients develop persistent AI agents.
A keynote for the 44th Annual Conference of the Physiotherapy Research Society. Argues that AI is now in contact with every part of the research process, and that the useful question is no longer whether researchers are using AI, but what they are using it for. Uses the PhD as a worked example to explore the difference between the artefact and the person becoming capable through the process, and argues that as AI becomes more capable, specifically human contributions — research taste, evaluative judgement, and the capacity to set direction — become more valuable, not less.
A keynote for the Royal College of Nursing's Education conference. Examines how generative AI has severed the inferential chain between assessment artifacts and the learning they were meant to evidence, and what nursing education needs to do structurally in response. The argument moves from the current AI landscape, through the premises of nursing formation, to why discursive responses (policies, declarations) cannot address a structural problem.
AI has disrupted doctoral education in two ways: the immediate question of AI-assisted writing, and the deeper question of what the PhD means when AI can conduct research from scratch. This post argues that the thesis was always a proxy for the person; evidence of an identity shift, not the thing being assessed in its own right.
More than 10,000 healthcare professionals have taken the courses I've created for Physiopedia Plus. This post focuses on the AI Masterclass for Healthcare Professionals Programme — a practical introduction to AI in clinical practice, education, and research. Physiopedia Plus members get full access, and a 30% discount code is included for new sign-ups.
Higher education's response to AI has focused on the artefact: detecting it, restricting it, and restoring confidence in what students produce. This essay argues that the structural features of problem-based learning — problem-driven inquiry, collaborative knowledge construction, facilitation over instruction, and metacognitive reflection — are the same conditions under which AI integration becomes educationally productive rather than substitutive. The alignment is structural, not retrospective: PBL was designed around these conditions before AI existed. The argument extends further: AI shifts what category of problem PBL can engage with, expanding access to wicked problems previously beyond students' reach. Investing in PBL's structural conditions is simultaneously investing in AI readiness.
AI assessment scales and similar policies are taxonomies of containment that ask how to protect existing assessment practices from AI, not whether those practices remain fit for purpose. This post argues that they're asking the wrong question, and examines what higher education might be asking instead, with particular implications for health professions education.
Academic offences committees are investigating the wrong party. When AI is integral to authentic professional practice, assessment that excludes it does not protect rigour — it tests performance in a professional context that no longer exists. Valid assessment measures what graduates will actually need to do; for most health professions graduates in 2025, that includes thinking well with AI. The accountability for assessment design lies with educators, not students.
A presentation for students participating in an EU-funded Blended Intensive Programme at Thomas More Hogeschool in Belgium. Examines how AI separates the production of artifacts from the learning they were meant to evidence, what problem-based learning already does differently, how AI changes group work and inquiry, and three practical shifts students can make in how they use AI within PBL.
The structural features of an information source that enable its knowledge claims to be challenged, traced back to evidence, and evaluated against the source's track record. Traditional sources carry it; generative AI largely does not.
Most universities have responded to AI by rewriting assessment policies and running prompt-writing workshops. Context engineering demands something different: infrastructure decisions that commit institutions to a direction. This post explains what context engineering involves, why it matters for health professions education, and why the gap between changing words and changing structures is where most institutions are stuck.
A mathematical framework demonstrating that AI tutoring systems with 10–15% error rates can achieve superior learning outcomes through dramatically increased engagement compared to more accurate but largely unused alternatives. Drawing on evidence from health professions education, this essay shows that the multiplicative relationship between accuracy and utilisation creates an accessibility paradox: imperfect but engaging systems outperform perfect but unused ones. The argument carries three critical qualifications—errors vary in consequence and safety-critical content demands high accuracy; generative AI poses distinctive epistemic challenges that may undermine conventional error correction mechanisms; and engagement is necessary but not sufficient for learning, with superficial use patterns capable of nullifying predicted benefits entirely. A framework for calibrating accuracy requirements to context and consequence is proposed.
Professional education curricula face a fundamental infrastructure problem: while comprehensively documented, they lack systematic queryability. This presentation introduces a three-layer architecture using graph databases as the source of truth for curriculum structure, supported by vector databases for content retrieval and the Model Context Protocol for stakeholder interfaces.
When AI can generate text, images, and ideas at scale, what remains distinctively human? This post argues that evaluative judgement—the capacity to assess what is worth creating, what deserves attention, and what matters—becomes the core human contribution in knowledge work. Drawing on research into evaluative judgement in health professions education, it explores how educators can make this capacity explicit and deliberately develop it, rather than treating it as an invisible by-product of experience.
The Chartered Society of Physiotherapy's annual Founders' Lecture, arguing that the question facing the profession is not what AI can do that humans cannot, but how practitioners shape the context in which human-AI collaboration happens. The talk opens with the disorienting evidence that AI now matches or exceeds clinicians on diagnosis, empathy, and ethical reasoning, then rejects the defensive 'sanctuary strategies' this provokes. Its central idea is context sovereignty: because language models are stateless, static, and contextless, the professional context a practitioner brings is not a comfort but a structural necessity — and controlling it is the distinctive human contribution. It closes with the coming shift to patient context sovereignty and the work of meaning-making in an ambiguous moment.
An invited presentation at the Lincolnshire LMC Getting It Done Conference exploring how generative AI functions as a virtual business consultant — supporting practice management, strategic planning, and operational efficiency for healthcare practices. The talk covers six use cases: developing online presence, targeting patient groups, enhancing patient experience, strategic service development, ethical implementation, and change management.
Health professions education faces a fundamental challenge: graduates are simultaneously overwhelmed with information yet under-prepared for complex practice environments. This essay introduces a theoretically grounded framework for integrating AI into health professions education that shifts focus from assessing outputs to supporting learning processes. Drawing on social constructivism, critical pedagogy, complexity theory, and connectivism, six principles emerge — dialogic knowledge construction, critical consciousness, adaptive expertise, contextual authenticity, metacognitive development, and networked knowledge building — to guide AI integration in ways that prepare professionals for the complexity and uncertainty of contemporary healthcare practice.
A presentation for the ADAPT International Conference 2023 on expertise and AI in professional education. Argues that generative AI is an unstoppable force — democratising access to professional knowledge and acting as a low-cost, personalised "expert mentor" — meeting the immovable object of universities and their traditional role as gatekeepers of expertise. Asks what formal professional education is for in a world where expertise is abundantly available, and argues for raising expectations through project-based learning and authentic assessment.
A presentation for the 2023 ENPHE Conference on coming to terms with AI in professional education. Argues that the dominant discourse traps us in a false choice between AI as saviour and AI as threat, when what the moment actually demands is a tolerance for ambiguity. Borrowing the antihero archetype, it reframes AI as neither hero nor villain but an ambiguous partner in new human–machine coalitions, and proposes that wise adoption is not a destination but the start of an iterative, cooperative process.