From Intention to Impact: Predictors and Learning Outcomes of AI Tool Adoption Among Saudi University Students

This study examines the predictors and consequences of artificial intelligence (AI) tool adoption among university students in Saudi Arabia, integrating the technology acceptance model (TAM), unified theory of acceptance and use of technology (UTAUT), as well as Cognitive Social Theory with AI-specific trust and ethical concern. Using a cross-sectional survey of 317 students from four geographically distributed public universities, we employed partial least square-structural equation modeling to test ten hypotheses. The results reveal that perceived usefulness, trust in AI, and social influence significantly predict AI adoption, while perceived ease of use and ethical concerns show no significant effects. Self-efficacy partially mediates the relationships between perceived usefulness and perceived ease of use with adoption, but not for trust. Notably, AI adoption significantly enhances academic performance and cognitive skill development. The findings contribute to technology acceptance literature by validating the combination of TAM, UTAUT, and social cognitive theory in AI adoption contexts, highlighting trust as a critical factor beyond traditional TAM constructs, and demonstrating AI’s positive educational outcomes. Practically, universities should emphasize AI’s usefulness, foster trust through transparency, and integrate self-efficacy training to maximize adoption benefits.