Asked whether universities can still guarantee a student actually learned something, one of the field's most respected assessment researchers said no. This is the honest version of the AI and assessment conversation in 2026 — not the keynote one.

Dale Leszczynski and Nick McIntosh work through the op-ed war between Kylie Moore-Gilbert and Cath Ellis, including the twist where the integrity academic's own piece was pulled for undisclosed AI use. They get into why AI detectors fail in both directions, and the Corbin and Dawson research on smart glasses that's dismantling the case for supervised exams. They look at what the student surveys actually show — near-universal AI use, but students reporting deeper learning when assessment restricts it — and the cognitive cost the sector is starting to take seriously. Then the turn toward what's being tried: the two-lane model, programmatic assessment, Leon Furze's reflection on three years of the AI Assessment Scale, and the Castlereagh Statement's call for coordination. There's no tidy answer here, by design.

00:00 Where the assessment debate actually sits in 2026
01:49 Moore-Gilbert's op-ed and "industrial-scale fraud"
03:02 Cath Ellis's rebuttal
04:05 The twist: an op-ed pulled for undisclosed AI
06:38 Why AI detectors don't work
08:37 The same problem inside newsrooms
09:33 Talk is Cheap: rules versus redesign
10:42 The sector blinks toward secure exams
11:35 A researcher's confession
14:07 Orals were "immune" — until the glasses
14:34 Mass-market smart glasses and assessment
15:41 Merleau-Ponty and dual transparency
16:30 A desert island and a pencil
17:38 From prohibition to inspection
19:08 What the student surveys show
19:55 Desaturation and false mastery
20:57 Use it or lose it
22:30 What's being tried: matrices and two lanes
23:20 Programmatic assessment
25:09 Lethal mutations and the AI Assessment Scale
27:51 A field maturing
28:14 The Castlereagh Statement
28:56 The escape hatch

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