Tag: ai-literacy
43 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.
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.
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.
A question that replaces "did you write this?"
Supervision has always run on an inference: if the writing is the student's, the work behind it probably is too. That's stopped holding, and detection tools don't repair it, because they're aimed at the document rather than the person. What I think breaks isn't integrity so much as calibration, which is a different, and more serious, problem to address.
Still Yours: a doctoral researcher's guide to AI
A book written with Benita Olivier for postgraduate researchers, on using AI without giving up the development a research degree exists to produce. It takes seriously the question researchers are already asking: why bother doing a research degree when AI can produce much of the work? We think the answer is that, while a thesis is the artefact, the researcher is the point. The practical middle is organised by the kind of relationship a researcher has with AI rather than by research stage: thinking partner, research assistant, writing collaborator, mediator of relationships, project manager. Springer Nature, expected November 2026.
The verification trap
Institutions 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.
A theoretical framework for integrating AI into HPE: a one-page guide
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.
What software engineers can teach us about AI in doctoral research
PhD 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.
Research harness
A research harness is a structured specification, negotiated between a doctoral researcher and their supervisor, of what an AI agent is for in a research project and how it's permitted to operate within it. It adapts the software engineering practice of harness engineering to doctoral inquiry, and treats the characteristic problems of AI use in research as the result of an agent working without a defined operating context.
The research harness: a one-page guide for doctoral researchers
A one-page reference guide for doctoral researchers and supervisors working with AI agents. Condenses the research harness framework into a practical quick-reference card: the seven components of a harness, what each one does, how to start building one, and what material form it takes.
A few thoughts on student use of AI
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.
The research harness
A structured specification of an AI agent's operating context in doctoral research. The harness names seven components — knowledge base, interpretive permissions, tools, authority, scope register, process record, and amendment protocol — so that a researcher working with an AI agent stays the analyst, judge, and author of their own inquiry. Published as a preprint and a one-page guide, now developing into a tested intervention.
We're comparing AI chatbot health advice to the wrong thing
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.
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.
Developing AI literacy
Developing AI literacy takes more than regular use. How people move from substitution through adaptation to transformation, why many stop at the first stage, and the three conditions that make progression possible: foundational orientation, authentic use, and deliberate reflection.
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.
AI Masterclass for Healthcare Professionals
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.
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.
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.
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.
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.
Prompt injection
Prompt injection is a technique in which instructions embedded in text cause an AI system to follow them as commands. In education it has appeared as a way of detecting AI use in assessment, which raises questions about authorisation and trust that the technique itself can't answer.
The hidden inefficiency in how we work with AI
Most academics use one AI model for everything, but different models suit different kinds of cognitive work. Choosing the right AI model for each step of a task can improve both efficiency and output quality. Michael Rowe describes how he noticed this in his own workflow, what splitting one workflow across three models looks like, and why the same logic applies when a university plans its AI strategy.
AI literacy development framework
A framework for embedding AI literacy development into existing modules and courses, enabling students to develop AI capability while learning disciplinary content.
AI literacy
What is AI literacy? Six connected capabilities, from recognising AI systems to judging when they serve your goals, that have to develop together.
The common architecture of literacy
The six dimensions of literacy that information, media, digital, data, and AI literacy frameworks share, from access and evaluation to contextual judgement, and why that means AI literacy builds on what educators already teach.
What a course can claim about AI literacy
Any claim that a course or programme develops AI literacy needs qualifying: literacy develops through sustained practice, is developmental and contextual, and can't be fully assessed at the end of a course.
Prompt engineering
Writing instructions for a language model in plain language, and refining them through conversation, until it produces what you need. The skill is closer to managing a colleague than to programming, which is why educators who supervise and give feedback already have most of it.
AI-forward
AI-forward describes an institution that treats AI integration as an ongoing strategic practice, evaluating and adapting as the technology changes, rather than as the one-off deployment of a finished solution.
Classroom policy on the use of generative AI
A template classroom policy for generative AI use that educators can adapt for their own modules and courses.
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.