11 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.
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.
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.
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.
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.
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.
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.
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.
A headless AI model runs non-interactively — no chat interface, no conversation. You pass it text, it returns output, and it exits. This makes AI tools composable with the same scripts and schedulers that have coordinated Unix processes for decades.
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.