Predicting higher education students' continued intention and self-reported use of generative AI tools in academic...

This study investigated the factors shaping Chinese higher education students' continued intention and self-reported use of generative artificial intelligence (GenAI) tools in academic work. Drawing on an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model, the study examined the roles of performance expectancy, effort expectancy, social influence, facilitating conditions, behavioural intention, use behaviour, and perceived knowledge. An explanatory sequential mixed-methods design was adopted. In the quantitative phase, survey data from 580 university students were analysed using structural equation modelling. In the qualitative phase, follow-up semi-structured interviews were conducted to explain and elaborate on the quantitative findings. The results showed that performance expectancy, social influence, and perceived knowledge significantly predicted students' behavioural intention to use GenAI, while facilitating conditions and behavioural intention were associated with self-reported use behaviour. Effort expectancy did not significantly predict behavioural intention, suggesting that ease of use alone may be insufficient to explain continued GenAI use among students who already perceive such tools as accessible. Interview findings further indicated that students used GenAI mainly for brainstorming, proofreading, explanation, and problem-solving, but they also expressed concerns about over-reliance, academic integrity, and the reliability of AI-generated content. The study contributes to GenAI continued-use research by showing that students' use of GenAI in academic contexts is shaped by expected usefulness, social influence, and their perceived knowledge of how to use and evaluate AI outputs. Practical implications are provided for designing AI literacy training, classroom guidance, and institutional policies for responsible GenAI use.