Study Shows AI Text Detectors Have Inherent Structural Limits That Disproportionately Harm Diverse Students

This is a constraint arising from population diversity that is logically independent of AI model quality and cannot be overcome by better detector engineering or technology.

Standard application of the variational characterisation of total variation distance to this composite null shows trade-off bounds that any text-only, one-shot detector with useful power must produce false accusations at a rate governed by the distributional overlap between student writing and AI output.

A subgroup mixture bound connects these quantities to observable demographic groups, providing a theoretical basis for the disparate impact patterns documented empirically.

We propose suggestions to improve policy and practice, and argue that detection scores should not serve as sole evidence in misconduct proceedings.