Enabling and Inhibitory Pathways of University Students' Willingness to Disclose AI Use: A Cognition-Affect-Conation Perspective

As artificial intelligence becomes increasingly common in higher education, universities are facing a challenge: how to encourage students to be transparent about their use of AI tools. This study explores the psychological factors that influence whether students choose to disclose their AI usage. By applying the Cognition-Affect-Conation (CAC) framework, the authors examine the mental and emotional barriers that prevent honesty, as well as the supportive environments that foster it.

How the Study Was Conducted

To understand the motivations behind student disclosure, the researchers used a mixed-methods approach. First, they conducted a quantitative survey of 546 university students, using structural equation modeling to analyze how cognitive perceptions and emotional responses relate to the intention to disclose AI use. Second, they performed qualitative, semi-structured interviews with 22 students to gain deeper insight into the survey findings and understand the personal experiences behind the data.

Key Drivers of Transparency

The research identifies "psychological safety" as the primary engine for honest disclosure. When students feel that their environment is fair and that they have the support of their teachers and the institution, they are significantly more likely to report their AI use. Essentially, when students feel safe and supported, they are more willing to be open about their academic processes.

Barriers to Disclosure

Conversely, the study highlights "evaluation apprehension"—the fear of being judged—as a major deterrent. This fear is fueled by perceived stigma, uncertainty regarding rules, and concerns about privacy. When students are unsure about how their use of AI will be viewed or penalized, they often resort to "strategic" or cautious behavior, hiding their AI usage to avoid potential negative consequences.

Implications for Higher Education

The findings suggest that the way universities handle AI policy has a direct impact on student behavior. Policy ambiguity and a lack of clear guidance create an environment where students feel they must hide their work to protect themselves. To promote responsible AI transparency, the authors argue that institutions must move beyond vague rules and instead foster supportive instructional practices and provide clear, consistent guidance that reduces the fear of negative evaluation.