Examining EAP Students' AI Disclosure Intention: A Cognition-Affect-Conation Perspective

The increasing integration of artificial intelligence (AI) in higher education has raised important questions regarding students' transparency in reporting AI-assisted work. This study investigates the psychological mechanisms underlying university students' willingness to disclose AI use by applying the Cognition--Affect--Conation (CAC) framework. A sequential explanatory mixed-methods design was employed. In the quantitative phase, survey data were collected from 546 university students and analysed using structural equation modelling to examine the relationships among cognitive perceptions, affective responses, and disclosure intention. In the qualitative phase, semi-structured interviews with 22 students were conducted to further interpret the quantitative findings. The results indicate that psychological safety significantly increases students' willingness to disclose AI use and is positively shaped by perceived fairness, perceived teacher support, and perceived organisational support. Conversely, evaluation apprehension reduces disclosure intention and psychological safety, and is strengthened by perceived stigma, perceived uncertainty, and privacy concern. Qualitative findings further reveal that institutional clarity and supportive instructional practices encourage openness, whereas policy ambiguity and fear of negative evaluation often lead students to adopt cautious or strategic disclosure practices. Overall, the study highlights the dual role of enabling and inhibitory psychological mechanisms in shaping AI-use disclosure and underscores the importance of supportive institutional environments and clear guidance for promoting responsible AI transparency in higher education.

This study investigates students' AI use concealment intention in higher education by integrating the cognition-affect-conation (CAC) framework with a dual-method approach combining structural equation modelling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA). Drawing on data from 1346 university students, the findings reveal two opposing mechanisms shaping concealment intention. The enabling pathway shows that perceived stigma, perceived risk, and perceived policy uncertainty increase fear of negative evaluation, which in turn promotes concealment. In contrast, the inhibitory pathway demonstrates that AI self-efficacy, perceived fairness, and perceived social support enhance psychological safety, thereby reducing concealment intention. SEM results confirm the hypothesised relationships and mediation effects, while fsQCA identifies multiple configurational pathways, highlighting equifinality and the central role of fear of negative evaluation across conditions. The study contributes to the literature by conceptualising concealment as a distinct behavioural outcome and by providing a nuanced explanation that integrates both net-effect and configurational perspectives. Practical implications emphasise the need for clear institutional policies, destigmatisation of appropriate AI use, and the cultivation of supportive learning environments to promote transparency.

This study examined how moral disengagement influences Filipino college students' intention to use ChatGPT in academic writing. The model tested five mechanisms: moral justification, euphemistic labeling, displacement of responsibility, minimizing consequences, and attribution of blame. These mechanisms were analyzed as predictors of attitudes, subjective norms, and perceived behavioral control, which then predicted behavioral intention. A total of 418 students with ChatGPT experience participated. The results showed that several moral disengagement mechanisms influenced students' attitudes and sense of control. Among the predictors, attribution of blame had the strongest influence, while attitudes had the highest impact on behavioral intention. The model explained more than half of the variation in intention. These results suggest that students often rely on institutional gaps and peer behavior to justify AI use. Many believe it is acceptable to use ChatGPT for learning or when rules are unclear. This shows a need for clear academic integrity policies, ethical guidance, and classroom support. The study also recognizes that intention-based models may not fully explain student behavior. Emotional factors, peer influence, and convenience can also affect decisions. The results provide useful insights for schools that aim to support responsible and informed AI use in higher education.