Who Gets Accused? AI Detection Bias, Academic Integrity, and the Uneven Field in Higher Education
When universities reach for AI detection tools to police academic integrity, they reach for instruments that are neither accurate nor neutral. Research shows that non-native English speakers, neurodivergent students, and those whose natural writing style resembles AI output face disproportionate false accusations. This article applies a moral panic framework to ask what the response to AI in higher education actually reveals — and who it disadvantages.
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