Beyond Usefulness: A Cognitive-Affective-Behavioral Analysis of Student Trust and Dependence on Generative AI in Higher Education

Abstract

With the growing integration of Generative Artificial Intelligence (GAI) in higher education, understanding how students cognitively, emotionally, and behaviorally engage with such tools is critical. This study proposes and tests a dynamic Cognition–Affect–Behavior (C-A-B) model to investigate the psychological mechanisms underlying students’ interaction with GAI. Survey data were collected from a Chinese university (N = 392). Structural equation modeling and cluster analysis were then conducted to examine the proposed model. The results indicate that cognitive factors—such as perceived functionality, metacognitive ability, and self-efficacy—affect behavior indirectly through emotional pathways. Trust enhances the influence of cognition on strategic behaviors, whereas anxiety weakens it, suggesting a bidirectional affective regulation process between cognition and emotion. Cluster analysis further identifies four user profiles—Dependency-oriented, Cautious Observers, Deep Integration Users, and Rational Adapters—linked to academic seniority and engagement styles. This study extends the traditional C-A-B model by introducing feedback loops between emotion and cognition, offering theoretical insights into AI-mediated learning. Practically, it highlights affect-sensitive and tailored strategies to foster reflective, autonomous, and effective use of GAI among students. Limitations and directions for future research are also discussed.

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