“Should I tell my teacher?” Student AI disclosure practices, stigma, and self-regulated learning in higher education
TL;DR
Examining undergraduate students’ AI disclosure practices suggests that institutional AI disclosure frameworks may benefit from addressing both policy compliance and the affective and disciplinary dimensions of students’ SRL decision-making.
Abstract
As generative artificial intelligence (GAI) tools become popular in education, questions about when and whether students disclose their AI use have emerged as a critical concern for academic integrity and self-regulated learning (SRL). Using SRL as an interpretive lens, we examine undergraduate students’ AI disclosure practices, investigating how their worries about negative consequences, AI usage behaviours, and demographic factors relate to their willingness to disclose AI use in academic settings. Drawing on survey data from 78 undergraduate students enrolled in an online education course, we developed study-specific survey items addressing worry, disclosure, and AI use. Given the developmental status of the instrument, we retained two preliminary composite indicators, worry and disclosure to teachers, and analyzed less internally consistent peer-disclosure and AI-use items individually. Our findings reveal a pattern of co-occurring worries: fear of teacher judgment and fear of social stigmatization are strongly correlated, and both are associated with students’ active concealment of AI use. Interpretively, we describe two contrasting response patterns among students: a transparent-disclosure pattern, in which disclosing AI use to instructors appears to reflect part of SRL processes, and an anxious-concealment pattern, in which heavier AI users report less transparency with teachers. A further finding is that anxiety is not associated with reduced disclosure to teachers directly, but is associated with increased peer-only sharing and complete secrecy. Exploratory analyses also suggest demographic patterns worth further study, with academic discipline showing the largest association with disclosure and a marginal indication that monolingual students may disclose less than multilingual students. Given the sample size, we reserve our interpretations and treat these as hypotheses for future work rather than established effects. These findings suggest that institutional AI disclosure frameworks may benefit from addressing both policy compliance and the affective and disciplinary dimensions of students’ SRL decision-making.