Tag: educational-technology
9 items with this tag.
What is the work?
A keynote for the Royal College of Nursing's Education conference. Examines how generative AI has severed the inferential chain between assessment artifacts and the learning they were meant to evidence, and what nursing education needs to do structurally in response. The argument moves from the current AI landscape, through the premises of nursing formation, to why discursive responses (policies, declarations) cannot address a structural problem.
Problem-based learning and the structural conditions for productive AI integration
Higher education's response to AI has focused on the artefact: detecting it, restricting it, and restoring confidence in what students produce. This essay argues that the structural features of problem-based learning — problem-driven inquiry, collaborative knowledge construction, facilitation over instruction, and metacognitive reflection — are the same conditions under which AI integration becomes educationally productive rather than substitutive. The alignment is structural, not retrospective: PBL was designed around these conditions before AI existed. The argument extends further: AI shifts what category of problem PBL can engage with, expanding access to wicked problems previously beyond students' reach. Investing in PBL's structural conditions is simultaneously investing in AI readiness.
AI and problem-based learning
A presentation for students participating in an EU-funded Blended Intensive Programme at Thomas More Hogeschool in Belgium. Examines how AI separates the production of artifacts from the learning they were meant to evidence, what problem-based learning already does differently, how AI changes group work and inquiry, and three practical shifts students can make in how they use AI within PBL.
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.
Claude Code
Anthropic's AI agent that works in a folder of files on your own machine, reading, editing, and checking them under your direction instead of answering in a chat window.
Open source software
Software whose source code is published under a licence that lets anyone read, change, and redistribute it — and what that means for the tools an institution depends on.
A bitter lesson for higher education
Rich Sutton's "bitter lesson" from AI research is that general methods using computation beat decades of carefully encoded human expertise. Higher education faces a parallel lesson about AI and assessment validity. Essays, reflective accounts, and other artefacts worked as evidence of learning mainly because they were hard to produce, and detailed briefs and rubrics now read as ready-made prompts for a language model. Handwritten exams, proctoring, and AI use scales all try to make artefacts hard to produce again, when the question worth asking is how we recognise learning at all.
Beyond document management: Graph infrastructure for professional education curricula
Professional curricula are extensively documented but not systematically queryable, creating artificial information scarcity that makes compliance reporting and quality assurance labour-intensive. This essay proposes a three-layer architecture — graph databases as the source of truth for curriculum structure, vector databases for semantic content retrieval, and a Model Context Protocol layer for stakeholder access — that transforms documentation into operational infrastructure. The architecture incorporates temporal versioning for longitudinal evidence, role-based access controls for multi-stakeholder environments, and internal quality audit against institutional policy alongside external regulatory compliance, enabling verification in hours rather than weeks.
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.