8 items with this tag.
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.
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 is permitted to operate within it. It adapts the software engineering practice of harness engineering to doctoral inquiry, treating the characteristic problems of AI use in research as problems of an absent operating context rather than of policy or capability.
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.
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.
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.
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 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.
AI has disrupted doctoral education in two ways: the immediate question of AI-assisted writing, and the deeper question of what the PhD means when AI can conduct research from scratch. This post argues that the thesis was always a proxy for the person; evidence of an identity shift, not the thing being assessed in its own right.