Tag: ai-integration
53 items with this tag.
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.
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.
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.
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.
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.
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.
The research harness, and bounding AI in doctoral work
This month's work centres on the research harness — a framework for bounded AI use in doctoral research — alongside a debate on AI assessment scales, a few reads worth your time, and quotes worth sitting with.
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.
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.
Path
Career progression, by design. Path treats professional development as a direction you choose, not a filing cabinet for what you've already done. Pick a standard to aim at — a promotion, a fellowship, a regulator's CPD requirement — gather evidence as you go, and watch a substantiated case for your next step build itself. One model serves CPD audits, fellowship and promotion applications, regulator validation, and student portfolios alike. A hosted prototype is live at pathcpd.com, in closed pilot and heading toward a commercial build.
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.
AI assessment scales are taxonomies of containment
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.
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.
Building arguments with AI
A field note on building arguments with AI: the brainstorming command I use to engage with any source, with an excerpt showing what a session looks like — Claude surfacing vault notes and Zotero sources, and why the conversation living in markdown matters.
How I review a week of reading notes with AI
Every week I annotate articles in Zotero, highlights in Reader, and podcasts in Snipd, all of which is synced to Obsidian. By Friday I have a week's worth of material, tagged and structured, but unreviewed. This post describes the weekly review command I built to surface what matters and create a reason to engage with it.
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.
Headless AI
A headless AI model runs non-interactively, with no chat interface and no turn-based conversation. You pass it text, it returns output, and it exits. That means it can be one step in an ordinary script or run on a timer, so tasks like grouping module evaluation comments by theme can happen every week without anyone opening a chat window.
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.
Preparing lecture slides with AI agents
I've been writing lecture slides in markdown for several years, mostly because I enjoyed working in structured formats and plain text. That decision turned out to matter in ways I didn't anticipate. When AI agents have access to your local filesystem, the format your teaching materials live in determines what's possible.
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.
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.
Working effectively with AI agents
What does it actually take to work with AI agents in a disciplined way, and how does someone get there from where they are now? This post draws on thinking from developers who've been working through this question longer than most academic knowledge workers have, and translates the hard-won lessons across. Three prerequisites emerge: planning before handoff, documentation treated as infrastructure rather than record, and domain expertise sufficient to evaluate what agents produce. The path in isn't a course or a tutorial — it's building something real with stakes attached.
AI agents
An AI agent is a system that autonomously executes multi-step tasks using language model reasoning — distinct from an AI assistant, which responds to individual prompts. Agents plan, act, observe results, and adapt, using tools such as file access, code execution, and web search. They perform best when given clear goals, explicit constraints, and well-prepared context.
AI agents for academic workflow
For the last few months, my screen has been split between Obsidian and a terminal, with two or three AI agents running in parallel tabs. This post describes what that shift in academic workflow looks like and what made it possible. The change is not simply additive: the work has shifted from execution to direction. What that distinction means in practice — and why it matters for those of us working in knowledge-intensive academic roles — is what I try to work out here.
Agentic workflows
The human role in agentic workflows: agents handle execution while humans decide what should be made, define what good looks like, and judge the results.
Seven principle for collaborating with AI
Seven principles for extended AI collaboration, distilled from a week-long project to restructure a large note collection using Claude Code. The principles cover goal-setting, understanding what AI can and cannot contribute, investing in planning conversations, adaptive planning, safety infrastructure, treating AI output as drafts, and expecting to learn something about your own thinking. Offered not as rules to follow but as patterns to recognise.
Building an AI-ready knowledge base
A detailed account of a week-long project to restructure 5,819 Obsidian notes using AI as a working partner. The project involved building a 23-category taxonomy, migrating thousands of legacy notes to a consistent metadata structure, and generating AI-written descriptions for every note in the collection. The piece describes not just what was done, but how extended planning conversations, external project documentation, and careful human review at each phase made the work tractable. The most unexpected outcome was that building infrastructure for a note collection required articulating, for the first time, precisely how I think about my academic field.
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.
Inference
The process by which a trained language model generates outputs; the computational work that happens each time you send a prompt and receive a response.
Token budget
The idea that different kinds of cognitive work have different computational costs in large language models, and that matching task complexity to model capability matters for both efficiency and output quality.
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.
Querying the Zotero database with AI
What happens when you query Zotero with AI and treat your whole reference library as context, instead of searching it one document at a time? This field note describes a proof of concept in which Claude Code reads the Zotero database file directly. The approach works, and what breaks shows how much your metadata practices matter. It also sets out the privacy trade-off of sending query results to a cloud-based model.
The quality of the challenge: AI as a thinking partner
Most discussions of AI in writing focus on output. This post describes using AI as a thinking partner while revising a theoretical framework paper for journal submission, asking it for critique, arguing back where it was wrong, and testing the draft against my own voice. The exchange changed the paper's structure and how it describes its methodology, and showed that the value lay in the quality of the challenge.
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.
Documentation debt
The accumulated cost of outdated, ambiguous, or poorly structured institutional knowledge — long paid invisibly by readers who work around the gaps, and exposed when AI agents read the same documents literally.
Organising your notes for AI: why retrieval isn't enough
Most advice on organising notes for AI treats it as a retrieval problem, as if the AI only needs to find the right documents. The harder problem is translation, making the relationships in your thinking machine-readable without losing what makes them yours. This post explains why search and retrieval fall short, shows what a typed link between two notes looks like, and suggests one small way to start.
Building an AI workflow for academics with structured documentation
Most advice on building an AI workflow for academics concentrates on writing better prompts. After several weeks of restructuring years of scholarly output with Claude Code, I've found the leverage lies in the structured context around the model — documentation that works as external memory, a cycle of testing rules and locking in the ones that hold, and the discipline of making implicit connections explicit. The same applies to institutions, where AI tools are only as reliable as the information architecture they're given.
Vertex
A curriculum knowledge graph that turns programme documentation into queryable infrastructure. Curriculum structure, learning outcomes, assessment alignment, and regulatory standards are modelled as a graph staff can interrogate in plain language — turning quality-assurance and compliance checks that take weeks into ones that take minutes. A Snapplify product I work on as a partner and consultant.
Similarities between AI and human thinking: What if we're the language models?
Discussion of AI and human cognition usually dwells on the differences. Turn the question around and the terminology of language models describes human thinking surprisingly well: context windows, biased training data, tokenisation, temperature, hallucination, system prompts, and pattern matching all have human counterparts, some of which health professions education already has names for. The similarities say something about our own cognitive architecture, and the strength of our resistance to them says something about what our professional identities depend on.
Hidden prompts in assignments: the security theatre of AI detection
Some lecturers now hide prompts in assignment materials so that any AI system reading them adds tell-tale keywords to its output. This post looks at what those tripwires assume about assessment, and why what they detect is carelessness with AI, not the absence of learning. The technique also invites students to turn it on AI marking, and starts an escalation neither side can win. The alternative is to ask what assessment is for when producing the artefact takes seconds.
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 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.