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.

翻译：本研究整合认知-情感-意动框架与结构方程模型(SEM)及模糊集定性比较分析(fsQCA)的双重方法，探究高等教育情境下大学生对AI使用的隐瞒意愿。基于1346名大学生的调查数据，研究发现两种塑造隐瞒意愿的对立机制。促进路径显示，感知污名、感知风险与感知政策不确定性会加剧负面评价恐惧，进而促进隐瞒行为；而抑制路径则表明，AI自我效能感、感知公平性与感知社会支持能够增强心理安全感，从而降低隐瞒意愿。SEM结果验证了假设关系与中介效应，fsQCA则识别出多重组态路径，揭示了等效性及负面评价恐惧在不同条件下的核心作用。本研究通过将隐瞒概念化为独立行为结果，并整合净效应与组态视角提供精细化解释，对现有文献作出理论贡献。实践启示强调需制定清晰的院校政策、消除合理使用AI的污名化，以及营造支持性学习环境以促进透明性。