4 items with this tag.
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.
Academic offences committees are investigating the wrong party. When AI is integral to authentic professional practice, assessment that excludes it does not protect rigour — it tests performance in a professional context that no longer exists. Valid assessment measures what graduates will actually need to do; for most health professions graduates in 2025, that includes thinking well with AI. The accountability for assessment design lies with educators, not students.
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.
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.