AI Misuse in Education Is a Measurement Problem: Toward a Learning Visibility Framework
The rapid integration of conversational AI systems into educational settings has intensified ethical concerns about academic integrity, fairness, and students' cognitive development. Institutional responses have largely centered on AI detection tools and restrictive policies, yet such approaches have proven unreliable and ethically contentious. This paper reframes AI misuse in education not primarily as a detection problem, but as a measurement problem rooted in the loss of visibility into the learning process. When AI enters the assessment loop, educators often retain access to final outputs but lose valuable insight into how those outputs were produced. Drawing on research in cognitive ...
| Authors | Eduardo Davalos, Yike Zhang |
|---|---|
| Primary category | cs.CY |
| Published | 8 March 2026 |
| Last updated at source | 8 March 2026 |
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