Technology
105 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.
What's new in Path, and a call for testers
Since July, Path has added an HCPC CPD profile export, the CSP Career Framework, link evidence, an achievement record, Trash and version history, and a complete export. I'm looking for more people to test it, with the understanding that things will almost certainly break.
AI infrastructure for academics: how earlier work makes later requests simple
I gave an AI agent two YouTube links and one sentence, and it returned two catalogued conference talks with slides, transcripts, and appraisal notes attached in Zotero. The request was short because months of earlier work sat behind it, including a fix from March and a tool from September. This post describes that session and an earlier one, and what they show about the difference between using AI to finish a task and using it in a way that leaves your system more capable than before.
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.
AI can only use what it can see
What changes when AI can reach your working context, plus the Taipei summit on AI in physiotherapy.
A personal design system for scholarly output
Almost everything I produce starts as a markdown file and ends in a different format — a slide deck, a handout, a Word document, this website — and until September 2026 I designed each one from scratch. I've replaced that with a personal design system with one visual language, one identity, and a template for each format, all reading the same token file. This post explains what the system is, how the pieces fit together technically, and what's still unfinished.
What the Quartz graph view reveals about this site
The graph view is one of the reasons I built this site with Quartz, and now that there's enough writing on it the links between pages have started to form a shape. The graph opens from the top bar, shows the site's writing as a network coloured by type, and lets you choose what's on it. This post shows how to read it, and what it reveals about the site: where the ideas cluster, which concepts hold the rest together, and which ones nothing connects to.
GitHub
GitHub is a website that hosts git repositories online and adds the tools people need to work on them together, including issues for discussion, pull requests for proposing and reviewing changes, and automation that can publish a project as a website. It was built for software developers, but anything kept as text files can live there, including teaching materials and scholarship.
Obsidian
Obsidian is a note-taking application that keeps every note as a plain markdown file in an ordinary folder on your own computer, and lets you link notes to one another. Because the notes are just files, they outlast the application, and any AI agent that can read files can read them without an export or a special connection.
Zotero
Zotero is a free, open source reference manager that stores the sources you collect with their citation details, PDFs, and notes. It also offers programming interfaces that let an AI agent search the library, read from it, and add to it, which turns a reference library into something an agent can check a draft against.
Distributed cognition
Distributed cognition is the idea that thinking happens across people, the tools they use, and the settings they work in, as well as inside individual heads. In healthcare it describes reasoning shared between clinicians, patients, records, and increasingly AI systems, and it shifts the question from what one clinician knows to how well the whole system thinks.
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.
Context window
The context window is the amount of text a language model can take into account at once, covering its instructions, the conversation so far, and any documents it has been given. It's the model's only working memory, and how full it is, and what sits where in it, shapes the quality of the answer.
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.
When agents start making decisions that influence outcomes
Two stories about AI agents making their own choices, and a much smaller project of mine that's seeing the same pattern.
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.
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.
YAML
YAML is a human-readable format for storing structured data as plain text. In knowledge management and publishing workflows, it appears most commonly as the frontmatter block at the top of markdown files, where it holds metadata — title, author, date, tags — that tools can read without parsing the document itself.
Distributed version control
Distributed version control tracks changes to files so that every contributor holds a complete copy of the project and its full history, with no central server to depend on. It allows offline work, parallel development, and recovery from any copy, and it lets partners who share a document, such as a practice assessment document, each keep the whole record of how it changed.
Git
Git is a distributed version control system that tracks changes to files over time. It records who changed what and when, allows you to move between earlier and later states of a project, and lets multiple people work on the same files without overwriting each other's contributions.
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.
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.
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.
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.
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.
Harness engineering
Harness engineering is the practice of building the full architectural scaffolding within which AI agents operate — structured documentation they can reason with, constraints that enforce invariants, and feedback loops that let them know when they've succeeded. It is distinct from prompt engineering, which shapes individual tasks, and from oversight, which monitors outputs after the fact. The harness is the infrastructure that makes delegation coherent at scale.
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.
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.
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.
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.
Context rot
Context rot, also called context drift, is the way a language model's output gets worse as its context fills with drafts, dead ends, and its own earlier mistakes. What causes it, why a larger context window does not fix it, and how to manage it in long pieces of work.
Context engineering for educators: infrastructure, not just policy
Most universities have responded to AI by rewriting assessment policies and running prompt-writing workshops. Context engineering asks for something different: decisions about system prompts, memory, retrieval, and connectivity that commit an institution to a direction for years. This post sets out what context engineering involves, why integrative, longitudinal, and values-based clinical education needs it, and why the gap between changing words and changing structures is where most institutions are stuck. It ends with three questions a programme team can ask about the AI tool it already uses.
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.
Claude Code
Anthropic's AI agent that works in a folder of files on your own machine, reading, editing, and checking them under your direction instead of answering in a chat window.
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.
Higher Education Reference Model (HERM)
A shared vocabulary for universities, set out in business, data, application, and technology reference models, and a practical starting point for making institutional knowledge readable by software and AI agents.
LaTeX
A typesetting system in which you write the structure of a document in plain text and the system lays it out — the standard for theses, mathematics and anything with hundreds of cross-references.
Markdown
A way of writing formatted documents in plain text — a hash for a heading, asterisks for emphasis — so the source stays readable and converts to anything.
Open source software
Software whose source code is published under a licence that lets anyone read, change, and redistribute it — and what that means for the tools an institution depends on.
Pandoc
A command-line converter that reads a document's structure in one format and writes it out in another — markdown to Word, Word to HTML, anything to PDF — with citations intact.
Plain text
Files made only of readable characters, with no formatting locked inside them. The format every tool can open, and the one that will still open in thirty years.
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.
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.
Single-hop reasoning
Answering a question in one step, by retrieving the passages most similar to it and generating a response from them. It's the default pattern in RAG systems, reliable when the answer sits in one place and weak when it has to be assembled from several.
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.
Beyond document management: Graph infrastructure for professional curricula
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.
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.
Documentation becomes infrastructure when AI agents are the readers
When AI agents consume documentation as operational input, it undergoes a category shift from reference material to operational architecture; inaccuracies no longer merely inconvenience readers, they cause system failures. This essay argues that the primary bottleneck for institutional AI integration is not AI capability but information architecture: how institutional knowledge is structured, maintained, and made available to AI systems. Documentation written for human readers cannot function as reliable AI input without deliberate restructuring around explicit relationships and rigorous maintenance workflows. Treating this transition as a governance imperative, rather than a technical afterthought, determines whether AI integration delivers on its institutional promise.
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.
Arms race dynamics in higher education
How arms race dynamics in higher education create adversarial relationships between institutions and students, and what drives these cycles
Contextual interoperability
Contextual interoperability is what lets you use your own notes, frameworks, and commitments with any AI model, local or hosted. It has two halves: the context has to be legible, with relationships a model can follow, and portable, held by you instead of inside one provider's system. With a curriculum mapping example from a pharmacy programme.
Intelligence as a service
A way of using AI in which you draw on a model for a particular task while your notes, documents, and working knowledge stay private and under your control.
MCP server
An MCP server is a small program that connects an AI assistant to one data source or service, such as a reference library, a folder of notes, or a calendar, through the Model Context Protocol. What the server offers sets the limit of what the assistant can see and do there.
Model Context Protocol
The Model Context Protocol (MCP) is an open standard for connecting AI applications to external data and tools through one common interface, in place of a custom integration for every pairing. It makes connections technically cleaner; whether they are wise ones is still a decision for the people who set them up.
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.
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.
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.
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.
GraphRAG
GraphRAG combines knowledge graphs with retrieval-augmented generation. Where ordinary RAG returns the passages most similar to a question, GraphRAG extracts the entities and relationships in a document collection and answers by following the connections between them, which is what questions about critiques, influences, or curriculum coverage need.
Multi-hop reasoning
Multi-hop reasoning with knowledge graphs: how AI answers questions that take several steps, and why recorded relationships make each step checkable.
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.
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
What is context sovereignty? The idea that people should own and author the context they bring to AI (their values, commitments, and ways of making sense of things) so that AI adapts to their thinking. How it differs from data sovereignty and model memory, its three principles, and what it means for students and educators.
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.
Avoiding innovation theatre: A framework for supporting institutional AI integration
Higher education institutions face persistent pressure to demonstrate AI engagement, often resulting in 'innovation theatre' — the performance of transformation without corresponding structural change. This essay presents a diagnostic framework distinguishing between performative and structural AI integration across four domains: governance and accountability, resource architecture, learning systems, and boundary setting. Unlike linear maturity models, it reveals gaps between institutional rhetoric and operational reality. Three legitimate strategic positions — incremental, selective, and transformative — help institutions move from accidental drift toward conscious choice. Treating AI integration as ongoing strategic practice rather than fixed deployment ensures institutions preserve agency over technology decisions aligned with institutional values.
Beyond document management: Graph infrastructure for professional education curricula
Professional curricula are extensively documented but not systematically queryable, creating artificial information scarcity that makes compliance reporting and quality assurance labour-intensive. This essay proposes a three-layer architecture — graph databases as the source of truth for curriculum structure, vector databases for semantic content retrieval, and a Model Context Protocol layer for stakeholder access — that transforms documentation into operational infrastructure. The architecture incorporates temporal versioning for longitudinal evidence, role-based access controls for multi-stakeholder environments, and internal quality audit against institutional policy alongside external regulatory compliance, enabling verification in hours rather than weeks.
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.
Taste and judgement in human-AI systems
Contemporary AI discourse often focuses on 'sanctuary strategies' — defensive attempts to identify uniquely human capabilities — positioning humans and AI as competitors for finite cognitive territory. This essay reframes human-AI relationships as embedded within complex cognitive ecologies where meaning emerges through interaction, and introduces 'taste' as a framework for cultivating contextual judgement: sophisticated discernment about when, how, and why to engage AI in service of meaningful purposes. Unlike technical literacy, taste development involves iterative experimentation and reflection, preserving human agency over value determination. By shifting from 'What can humans do that AI cannot?' to 'How might AI help us do more of what we value?', the essay builds a case for abundance-oriented human-AI partnership.
The learning alignment problem: AI and the loss of control in higher education
Higher education's focus on prompt engineering — teaching technical skills for crafting AI queries — represents a misunderstanding of learning. This essay argues that prompts emerge from personal meaning-making frameworks, not technical mechanics, and that the institutional impulse to control AI interaction reveals a 'learning alignment problem': systems optimising for measurable proxies like grades rather than authentic curiosity. Drawing parallels to AI safety's value alignment problem, it shows how AI exposes that many assignments were already completable without genuine intellectual work. Universities must shift from control to cultivation paradigms, recognising that learning is personal and resistant to external specification, ensuring AI becomes a partner in human flourishing rather than a tool for strategic performance.
Context sovereignty for AI-supported learning: A human-centred approach
This essay proposes 'context sovereignty' as a framework for maintaining human agency in AI-supported learning, arguing that context engineering — not just prompting — is the key to meaningful human-AI collaboration.
A theoretical framework for integrating AI into health professions education
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.
Technological nature of language and the implications for health professions education
Language is humanity's first general-purpose technology, developed to extend cognitive capabilities beyond biological limits. Large language models represent the latest evolution in a continuum stretching from spoken language through writing and print to digital text, extending language's capabilities through unprecedented scale, cross-domain synthesis, and cognitive adaptability. For health professions education, this framing shifts priorities from knowledge acquisition toward cognitive partnership and adaptive expertise. It demands a reconceptualisation of AI literacy — moving beyond technical prompting to understand how these tools shape reasoning — and requires assessment to evaluate students' capacity for collaborative problem-solving. Understanding LLMs as language technology offers a middle path between uncritical enthusiasm and reflexive resistance.
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.