Tag: generative-ai
48 items with this tag.
Practical AI for private practice
A four-hour online masterclass for rehabilitation clinicians in the Kinatex network, run as four hands-on sessions on patient education, professional development, admin and business, and closing with patients who bring their own AI into the clinic.
AI fluency
AI fluency is the ability to work with AI systems in ways that are effective, efficient, ethical, and safe. The AI Fluency Framework describes it as four connected competencies: delegation, description, discernment, and diligence. It builds on AI literacy and goes further, into the practice of working with AI on real tasks.
Processing fluency
Processing fluency is the ease with which the mind takes in information. People read that ease as a sign that something is true, familiar, or good, which is why fluent AI-generated text is persuasive whatever its accuracy, and why markers who equate clear writing with clear thinking need to separate the two.
Sycophancy
Sycophancy is a language model's tendency to tell you what you want to hear, agreeing with your view, praising your work, and backing down when challenged, even when you're wrong. It comes from how models are trained, and it matters most when you're relying on the model to catch a mistake.
Token
A token is the unit a language model reads and writes in, usually a short word or a fragment of a longer one. Context windows, usage limits, and prices are all counted in tokens, so the size of a task in tokens decides what a model can take in at once and what it costs.
Hallucination
When a language model produces a fluent, confident statement that is false, why it happens, and why it is harder to catch than other AI errors.
Making sense of AI in clinical practice: ATOCP at BOA Congress
An invited talk in the ATOCP session at the BOA Annual Congress 2026 in London. It looks at what AI can already do in clinical work, why the usual responses to it leave clinicians as spectators, and what physiotherapists bring that AI systems don't have, which is context. The second half turns to patients, who are already using AI before they arrive, and asks what changes in the consultation when both sides bring their own context and their own agents. It closes with three things to do the next working day.
AI-powered practice growth
A full-day workshop for the International Private Physiotherapy Association's business meeting in Hamburg, run as four linked sessions: how AI actually works and how to prompt it well, building content for a real client group, getting that content found and trusted, and drafting a three-year business plan. Delegates build a single practice file across the day, using their own practice as the input, and leave with a persona, a piece of content in five formats, a communication plan, and a draft growth plan. Every exercise runs on free-tier tools.
Better than, or better with?
A panel presentation for the South African Society of Physiotherapy's 2026 Symposium, on ethics and the use of AI in physiotherapy. Argues against framing AI in healthcare as a capability contest to be won or lost, and asks a more uncomfortable question: whether keeping the human in the loop is sometimes less about patient benefit than about our own discomfort with the alternative. Closes with concrete ways to bring AI into the clinical conversation rather than manage it as a threat.
New therapeutic alliances
A panel presentation for the South African Society of Physiotherapy's 2026 Symposium, on AI and the future of physiotherapy education. Patients are already arriving at consultations having consulted AI first, and are increasingly capable of getting genuinely useful health guidance from it. The talk asks what this means for practice, research, and education — not whether AI will replace clinical judgement, but how the profession prepares students and practitioners for patients who bring an AI-shaped health literacy into the room.
What happens when AI joins the leadership team?
A conversation with Sharon Black on the Leadership Biscuits podcast about what AI asks of leaders in health and social care. The argument is that AI is not a technology project to be handed to IT, because it reaches into decision-making, workflow, governance, education and patient care. We talk about using AI as a thinking partner rather than a productivity tool, why augmentation is a more useful frame than replacement, what universities need to rethink about assessment, and why leaders who have not used these systems themselves cannot govern them well.
Introducing Path, a CPD portfolio app
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.
AI and the learning alignment problem
An 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.
Building a CPD app with Claude Code
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.
Making sense of AI in clinical practice: MACP webinar
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.
Beyond the artifact: AI and the future of research
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.
What is the work?
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.
The PhD is a process of becoming: Reimagining AI and PhD assessment
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.
Transform documents with AI
Language models can transform documents into interactive tools in minutes. This post walks through a concrete example, turning a 21-page Word questionnaire into a working web app, and reflects on what that capability makes possible.
Problem-based learning and the structural conditions for productive AI integration
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.
Designing AI out of assessment should be an academic offence
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.
Why AI writing sounds convincing, and why that's noise
AI-generated text is easy to read whether or not its content is accurate or well reasoned, and easy-to-read text feels true. Fluency was once a reasonable trace of genuine thinking, since a student who wrote clearly had usually thought clearly, but that relationship no longer holds. The AI literacy response of teaching output evaluation is a temporary fix, because as models improve, output quality converges on expert level across every artefact we care to measure. Spotting today's failure modes leaves open what you'll do when they're gone.
The writing was never the point: AI and academic writing identity
When AI could write everything I'd ever written, I had to ask: what had I been doing all this time? The answer changed how I understand both writing and AI — and what it means to be a scholar in a world where words are cheap.
Why AI makes spelling mistakes
Claude produced the word "contribuves" in a piece of writing, which is obviously not a real word. This is a different kind of error than hallucination, and the distinction matters.
AI and problem-based learning
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.
Epistemic accountability
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.
Building AI personas for professional practice
Most advice on AI effectiveness focuses on prompt engineering. The real leverage comes from somewhere less obvious; knowing your professional commitments clearly enough to turn them into context an AI can work within. This post describes how to build AI personas for professional practice — structured documents that compress your values, frameworks, and evidence into a form an AI agent can actually use.
Claude deleted my file. It was my fault. But Claude took the blame.
A field note on the time Claude deleted my file. The agent followed my instructions precisely and that was the problem. A reflection on a different kind of AI failure mode, and what the model's apology reveals about where responsibility actually sits.
Introduction to AI with Copilot
An internal staff development session for the CPC team introducing AI through Microsoft Copilot. Covers what AI is and isn't, safe working practices, structured prompting with the RGID heuristic, and hands-on practice — with the goal of each participant leaving with one specific task to try that week.
AI dependency in practice: When the tools become infrastructure
A field note on what the recent Claude outage revealed about where I am on the dependency curve, and what the difference between a session limit and an outage tells you about infrastructure.
AI agent governance in higher education: Building the institutional harness
The previous posts described what makes agentic workflows coherent at the individual level: a plan, documentation as infrastructure, and domain expertise that can evaluate outputs. Together, these form an informal harness; the conditions within which delegation stays accountable. At institutional scale, a personal harness is not enough: multiple people directing agents without shared constraints produce compounding drift that no amount of human oversight can track. This post examines what AI agent governance in higher education actually requires, and why a harness, not better oversight, may be the right frame.
Vibe coding
Vibe coding describes using AI tools without keeping genuine accountability for what they produce: accepting outputs without the scrutiny, direction, or judgement needed to evaluate and improve them. Simon Willison drew the key distinction between vibe coding and vibe engineering, which uses the same tools while staying accountable for every output.
Is a smaller AI model good enough?
A field note on switching my default in Claude Code from Opus 4.6 to the smaller Sonnet 4.6, testing it on a task I'd normally give the larger model, and finding it good enough so far.
Developing evaluative judgement for a time of generative artificial intelligence
Source details Bearman, M., Tai, J., Dawson, P., Boud, D., & Ajjawi, R. (2024).
Talk is cheap: why structural assessment changes are needed for a time of GenAI
Source details Corbin, T., Dawson, P., & Liu, D. (2025). Talk is cheap: why structural assessment changes are needed for a time of GenAI.
The wicked problem of AI and assessment
Source details Corbin, T., Bearman, M., Boud, D., & Dawson, P. (2025). The wicked problem of AI and assessment.
AI tutor accuracy in health professions education: The accuracy-engagement paradox
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.
Embeddings
An embedding is a list of numbers, learned during training, that represents a piece of text so that similar meanings get similar numbers. Embeddings let AI find material by meaning rather than by matching words, but they record that ideas are associated, not how they are related.
Retrieval augmented generation
A technique that improves a language model's answers by retrieving relevant passages from your own documents and adding them to the prompt, so the model answers from sources instead of from memory alone.
Vector database
A database that stores embeddings, so that it can find content by meaning instead of by matching words. It's the storage layer behind most RAG systems.
System prompt
Instructions placed in front of every conversation with an AI model, setting how it behaves, what it knows, and what it won't do, usually without the user seeing them.
Large language models
A plain explanation of large language models: what they are, how they are trained, where their limits come from, and what their ability to produce fluent text means for assessment in health professions education.
A bitter lesson for higher education
Rich Sutton's "bitter lesson" from AI research is that general methods using computation beat decades of carefully encoded human expertise. Higher education faces a parallel lesson about AI and assessment validity. Essays, reflective accounts, and other artefacts worked as evidence of learning mainly because they were hard to produce, and detailed briefs and rubrics now read as ready-made prompts for a language model. Handwritten exams, proctoring, and AI use scales all try to make artefacts hard to produce again, when the question worth asking is how we recognise learning at all.
Context engineering
How context engineering differs from prompt engineering: building the knowledge and structure a language model needs to reason well about your work.
Context sovereignty: building human-AI coalitions in physiotherapy
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.
AI and the business of practice
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.
An unstoppable force meets an immovable object
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.
With great power comes great ambiguity
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.