Students fear that declaring AI use will lead to lower marks even when permitted, creating a 'Disclosure Trap' that drives AI use underground¶

Claim · All claims
Evidence · 1 study · associational r1 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Research into student attitudes finds students fear penalties for declaring AI use regardless of institutional policy, creating a perverse incentive against ethical transparency. → Md Zarzees Uddin Shah Chowdhury and Samin Rahman Khan 2026

Evidence¶

Md Zarzees Uddin Shah Chowdhury and Samin Rahman Khan 2026¶

Md Zarzees Uddin Shah Chowdhury and Samin Rahman Khan. (2026). Evaluation in the Age of AI: Output as Evidence of Learning. https://arxiv.org/abs/2608.22660 doi:10.48550/arxiv.2608.22660

q2 · i? · associational · r1

The article reports, citing Gonsalves, research into student attitudes showing "students fear that declaring AI use, even if permitted, will lead to lower marks". The authors argue that in the absence of clear disclosure protocols, grader bias may penalize transparency, driving AI use underground.

"students fear that declaring AI use, even if permitted, will lead to lower marks, regardless of institutional policy [9]."

Discussion¶

Related Claims¶

• Students frequently do not complete mandatory AI use declarations, perceiving them as admissions of wrongdoing (attributed to Gonsalves) — a narrower finding that bears on this claim
• Fear that AI will replace jobs creates significant barriers to workforce engagement in AI literacy training — related
• Disclosure status alone often changes little in substantive classification, and false declarations change fewer classifications than expected — related
• AI use threatens students' computing identities as competent, hardworking, and ethical, with students identifying as cheaters, lazy, or incompetent even where AI is permitted — related
• Universities converge on disclosure and attribution expectations as the condition distinguishing legitimate AI assistance from misrepresentation — related
• Shame and guilt regulate the visibility of AI use: students engage in hiding behaviors and selective disclosure rather than open disclosure — a narrower finding that bears on this claim