Enabling and Inhibitory Pathways of Students' AI Use Concealment Intention in Higher Education: Evidence from SEM and fsQCA

TL;DR

This study explores the psychological and contextual factors influencing students' intentions to conceal AI use in higher education, using SEM and fsQCA to identify enabling and inhibitory pathways.

Contribution

It introduces a dual-method approach combining SEM and fsQCA to analyze concealment intention, highlighting multiple pathways and the central role of fear of negative evaluation.

Findings

Perceived stigma, risk, and policy uncertainty increase concealment via fear of negative evaluation.

AI self-efficacy, fairness, and social support reduce concealment by enhancing psychological safety.

Multiple configurational pathways influence concealment intention, emphasizing the importance of context.

Abstract

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.