Expectancy and value beliefs predicting generative AI use: evidence from Chinese university faculty

Background Guided by expectancy–value theory, this study investigated how Chinese university teachers’ Generative AI (Gen AI)–specific task values (intrinsic, utility, attainment), perceived Gen AI costs, and Gen AI self-efficacy predict their behavioral intention and frequency of Gen AI use. Methods From two universities in China, 365 faculty members completed an online survey. Results Structural equation modeling revealed that utility value and self-efficacy positively predicted behavioral intention, while only self-efficacy was a significant predictor of actual usage frequency. Task values and perceived cost, aside from utility value, did not significantly influence outcomes. Conclusion These findings highlight the central role of self-efficacy and perceived utility in motivating teachers’ adoption of Gen AI, offering theoretical insights for expectancy–value research and practical guidance for professional development initiatives aimed at fostering effective integration of AI in higher education.