Detecting AI in a student’s work: why reliability is not the question
Detectors have improved, and that settles nothing: a probability about a text cannot ground a decision about a student.
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What makes a response work: the information, not the grade
More than 38% of feedback effects are negative. What makes the difference is how much information is carried, not the grade.
Acceptance is not use: what surveys on AI in schools actually measure
Acceptance surveys measure an intention validated against a self-report. Self-reported use tracks logged use poorly.
Marking with AI: compare it with our real errors, not with perfection
A century of research on human marking, and what it means for using AI as a second reader.
A correct item is not necessarily aligned: three questions for judging an assessment item
Alignment, cognitive demand, quality: three separate questions for judging an item against the PER.
Protect the effort that makes learning happen: what to hand to AI, what to keep demanding
Desirable difficulties and the first studies on AI in class: what effort does for learning.
Preparing fast with AI: the real work is checking
AI saves preparation time; the real work is checking the objective, the level of demand and accuracy.
What AI really changes for teachers who prepare and assess
Prepare, assess, learn: three shifts AI brings to teachers’ work.