Teaching
9 items with this tag.
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.
The verification trap
Institutions everywhere now insist on the same rule — use AI, but always verify its output. Yet verifying AI output is hard, slow, and often inconclusive, even in domains where you're competent to judge it. This post pushes that advice to its limit to see what breaks, and finds that as models improve, the verification mandate quietly trains the opposite of the scepticism it intends.
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.
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.
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.
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.
From teaching to learning: Rethinking education for a world of information abundance
Traditional education systems are structured around teaching, assuming it inevitably produces learning. This essay argues that this unidirectional model — knowledge flowing from expert to novice — no longer suits a world of information abundance and AI disruption. A networked, learner-centred approach offers an alternative, reconceptualising learning as a complex process where educators shift from knowledge authority to learning facilitator, enabling diverse participants to contribute to collective understanding. This transformation is urgent as AI develops capabilities once exclusive to human experts, and requires reimagining institutional structures around learning networks rather than teaching hierarchies to prepare graduates for complexity and uncertainty.
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.