36 items under this folder.
Academic publishing treats scholarship as a finished, individually owned artefact. This post describes a writing and publishing workflow built on a different premise: that a scholarly corpus could work like an open source project — readable, contributable, forkable, and never permanently owned by anyone.
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.
More than 10,000 healthcare professionals have taken one 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.
AI assessment scales and similar policies are taxonomies of containment that ask how to protect existing assessment practices from AI, not whether those practices remain fit for purpose. This post argues that they're asking the wrong question, and examines what higher education might be asking instead, with particular implications for health professions education.
Academic offences committees are investigating the wrong party. When AI is integral to authentic professional practice, assessment that excludes it does not protect rigour — it tests performance in a professional context that no longer exists. Valid assessment measures what graduates will actually need to do; for most health professions graduates in 2025, that includes thinking well with AI. The accountability for assessment design lies with educators, not students.
AI-generated text is fluent regardless of whether its content is accurate or well-reasoned. Fluency was once a reasonable trace of genuine thinking — a student who wrote clearly had usually thought clearly. That relationship no longer holds. Worse, the AI literacy response of teaching output evaluation is a temporary fix: as models improve, output quality converges on expert-level across every artefact we care to measure. The question isn't how to spot current failure modes. It's what you'll do when those failure modes are gone.
Dense periods leave a residue — not the urgent things, which get handled, but everything else that quietly accumulates. The email backlog anxiety this produces isn't about workload. It's about the unbounded unknown. Understanding the difference changes how you approach the clearance.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
A field note on switching from Claude Opus 4.6 to Sonnet 4.6 as my default in Claude Code, and what I'm noticing after the first hour.
Most academics treat AI models as interchangeable general-purpose tools. They aren't. Different models have different characteristics that make them better suited to particular kinds of cognitive work, and matching tasks to those characteristics may improve both efficiency and output quality. This post explores what that looks like in personal workflows and how the same logic scales to institutional AI strategy.
What happens when you query the Zotero database with AI, treating your entire reference library as context rather than searching it document by document? This field note documents a proof of concept using Claude Code to read a Zotero SQLite database directly. The approach works but what breaks reveals how much your metadata practices actually matter.
Most universities have responded to AI by rewriting assessment policies and running prompt-writing workshops. Context engineering demands something different: infrastructure decisions that commit institutions to a direction. This post explains what context engineering involves, why it matters for health professions education, and why the gap between changing words and changing structures is where most institutions are stuck.
Most discussions of AI in writing focus on output. This post describes a different experience—using AI as a thinking partner to challenge my choices and claims during a writing session.
Most advice on organising your notes for AI treats it as a retrieval problem. The harder problem is translation; making your thinking machine-readable without losing what makes it yours. Contextual interoperability is the infrastructure that enables genuine AI collaboration in scholarly work.
Most conversations about AI focus on what it produces. This post describes what an AI workflow for academics actually looks like in practice — building structured context through documentation, iteration, and judgement that makes AI collaboration increasingly effective over time. Drawing on several weeks of restructuring scholarly output with Claude Code, I describe the iteration cycle, the role of documentation as external memory, and what the process reveals about the relationship between explicit information architecture and productive AI collaboration.
The discourse around AI and human cognition tends to focus on differences, but what happens when we invert the question and use LLM terminology to explore the similarities between AI and human thinking? This post examines parallels between AI cognitive architecture and human thinking—context windows, training data bias, tokenisation, temperature, hallucination, and pattern matching—not to claim that humans are language models, but to ask what these similarities reveal about our own cognitive processes and why we are so invested in denying them.
When educators embed hidden instructions in assessment materials to detect AI use, they import adversarial security thinking into educational relationships. This post examines what AI tripwires reveal about institutional assumptions (i.e. that assessment is about artifact authentication rather than learning measurement) and argues that this approach creates escalating countermeasure dynamics while only detecting carelessness, not genuine disengagement. The alternative requires rethinking what assessment is actually for in an era when artifact production has become trivially automatable.
Rich Sutton's 'Bitter Lesson' from AI research—that general methods leveraging computation outperform human-crafted knowledge—has a direct parallel in education. When AI can produce the kinds of artefacts that assessments have traditionally relied on, it exposes a fundamental problem we have long ignored: we were never really measuring learning, we were measuring the difficulty of producing certain artefacts. This post explores what the Bitter Lesson means for assessment design in health professions education, and why AI makes it impossible to continue pretending otherwise.
When AI can generate text, images, and ideas at scale, what remains distinctively human? This post argues that evaluative judgement—the capacity to assess what is worth creating, what deserves attention, and what matters—becomes the core human contribution in knowledge work. Drawing on research into evaluative judgement in health professions education, it explores how educators can make this capacity explicit and deliberately develop it, rather than treating it as an invisible by-product of experience.
Generative AI presents serious ethical challenges in education—to academic integrity, to equity, to the nature of learning itself. This post acknowledges these concerns while arguing that AI also represents an unprecedented opportunity for learning at scale, particularly for the kinds of personalised, adaptive learning that have always been theoretically desirable but practically impossible to deliver. For health professions educators committed to expanding access to quality education, this opportunity deserves serious, open-minded consideration.
Academic culture has converged on the peer-reviewed journal article as the default unit of scholarly output, creating a hierarchy that excludes many valuable forms of intellectual work. This post makes the case for essays as a legitimate form of scholarship—not as a lesser alternative to empirical research, but as a distinct mode that enables exploration, synthesis, and engagement with audiences that traditional publishing cannot reach. Drawing on Boyer's model of scholarship, it argues for a more generous conception of what counts as scholarly contribution.
AI meeting scribes are increasingly being adopted as productivity tools, automatically transcribing and summarising organisational meetings. But who controls these records, and who benefits from perfect organisational memory? This post explores how AI meeting scribes can entrench existing power dynamics by giving those in authority unprecedented access to communication patterns, informal decision-making, and dissent—all rendered visible and retrievable without those present realising the implications for how organisations are governed.
Academic publishing has converged on the written journal article as the dominant form of scholarly output, but knowledge has always been transmitted through conversation, dialogue, and oral communication. This post explores whether audio scholarship—podcasts, recorded dialogues, oral histories—deserves recognition as legitimate scholarly work. Drawing on Boyer's model of scholarship, it argues that format matters less than the rigour, intention, and intellectual contribution behind the work, and considers what it would take for academic culture to broaden its definition of what counts.
Most commentary on AI in education focuses on what AI cannot do, or catalogues its failures as warnings. This post argues for a different approach—instead of performative critique, demonstrate thoughtful use in your own practice. By modelling considered, reflective engagement with AI tools, health professions educators can critique from experience rather than speculation, help shape how AI is integrated into professional education, and play a better game than the one they're currently losing.