An Empirical Study on College Students’ Behavioral Intention to Use Generative Artificial Intelligence: An Integrated Model Based on the Innovation Diffusion Theory and Trust Theory

This study, grounded in Innovation Diffusion Theory and Trust Theory, examines the factors influencing college students’ intention to adopt generative AI. A survey of 586 randomly selected students gathered self-reported data on eight factors: relative advantage, compatibility, complexity, observability, trialability, perceived usefulness, trust, and behavioral intention. Using structural equation modeling (SEM), the study analyzed the relationships among these factors. The results showed that relative advantage did not significantly impact perceived usefulness or behavioral intention. Complexity negatively affected behavioral intention but no significant impact on perceived usefulness. Compatibility, observability, and trialability positively influenced both perceived usefulness and behavioral intention. Perceived usefulness positively affected trust and behavioral intention, and trust also positively influenced behavioral intention. Mediation analysis showed that trust partially mediated the relationship between perceived usefulness and behavioral intention. Additionally, perceived usefulness acted as a partial mediator in the relationships between compatibility, observability, trialability, and behavioral intention. Gender moderated the relationship between perceived usefulness and trust, indicating gender differences in trust-building. The study offers valuable insights into students’ behavioral intention to use generative AI and offers practical recommendations for promoting the technology in education.