A student submits an assignment they wrote entirely themselves — and is flagged as a cheat by software that cannot actually prove anything at all. As generative AI reshapes how students learn, write, and think, schools and universities have reached for AI detectors as a quick fix for an old fear: academic dishonesty. But the evidence is damning. Detectors misfire on non-native English speakers, neurodivergent students, and simply careful writers, while sophisticated cheaters slip through undetected.
How Artificial Intelligence Is Reshaping Trust, Cheating, and Learning is a clear-eyed investigation into what happens when institutions trade fair process for the illusion of certainty — and why the resulting culture of suspicion is failing the very students it claims to protect. Drawing on published research, court rulings, and real policy from universities and education bodies worldwide, this book moves past outrage to offer something more useful: a practical path forward. It explains why detection technology cannot be trusted as sole evidence, how meaningful assessment can make learning visible without surveillance, and what students, parents, teachers, and institutions each need to do differently when AI use is suspected.
