Scholarship
26 items with this tag.
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.
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.
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.
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.
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.
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.
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.
The research harness: a framework for bounded AI use in doctoral work
Current responses to AI use in doctoral research locate the problem either with institutions through policy, or with students through judgement and AI literacy. This essay argues that neither response addresses the structural source of the difficulty. The characteristic problems of AI use in doctoral research are not problems of policy or capability; they are problems of working with an agent in the absence of a defined operating context. Drawing on software engineering practice, it develops the concept of the research harness: a structured specification, negotiated between researcher and supervisor, of what an AI agent is doing in a doctoral project. The harness has seven components — knowledge base, interpretive permissions, tools, authority, scope register, process record, and amendment protocol — and can be entered at a minimal level and developed iteratively alongside the work.
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.
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.
Research taste
Research taste is the cultivated capacity to recognise which problems are worth pursuing, which collaborators will amplify your work, and which under-explored areas have genuine leverage — before you can fully prove any of those judgements.
Research industrial complex
The research industrial complex describes the self-reinforcing system of incentives across universities, funding bodies, journals, and publishers that rewards publication volume and impact metrics over meaningful scientific progress. The term draws on Eisenhower's military-industrial complex to show how interconnected institutional interests can sustain a system that works against its own stated mission.
An open scholarship workflow: What academic publishing can learn from open source
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.
A new home for my writing
Introducing my new platform for writing on AI in health professions education. It explains what's happened to usr/space and Head Space, what this newsletter will cover going forward, and links to my most recent presentations and posts.
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.
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.
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.
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.
Open access licensing
Open licences, mostly Creative Commons, state in advance what others may do with a work — read it, share it, adapt it, build on it — so nobody has to ask. The Creative Commons licence on teaching materials decides whether a colleague can adapt a resource for their own programme or has to rebuild it from scratch.
Boyer's model of scholarship
A multidimensional framework for scholarship spanning discovery, integration, application, and teaching.
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.
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.
Publishing with purpose: Using AI to enhance scientific discourse
The introduction of generative AI into scientific publishing presents both opportunities and risks for the research ecosystem. This essay argues that scientific journals must transform from metrics-driven repositories — prioritising publication volume over meaningful progress — into vibrant knowledge communities using AI to facilitate discourse. AI can support this by surfacing connections between research, making peer review more dialogic, and enabling multimodal knowledge translation. Meaningful change requires coordinated action across institutions, funding bodies, and journals willing to prioritise scientific progress over quantitative metrics. By reimagining journals as AI-supported communities rather than article-processing platforms, the research ecosystem can better serve scientific knowledge development and clinical outcomes.