Factors influencing Chinese pre-service teachers' adoption of generative AI in teaching: an empirical study based on UTAUT2 and PLS-SEM

Although Generative Artificial Intelligence (GAI) has demonstrated significant potential in education, there is a lack of research on pre-service teachers' behavioral intentions toward GAI. This study is based on the UTAUT2 model and, for the first time, introduces perceived risk as a key variable to systematically investigate the factors influencing Chinese pre-service teachers' behavioral intentions and future usage of GAI in teaching. The innovation of this study lies in its analysis of the unique needs of pre-service teachers across disciplines, which reveals critical factors in technology acceptance. These findings offer new theoretical insights and practical guidance for the effective application of GAI in K-12 education. The study involved 563 pre-service teachers from three renowned teacher training universities in China. Data were collected using an online questionnaire platform and analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The results indicated that effort expectancy, social influence, hedonic motivation, and habit significantly enhanced pre-service teachers' behavioural intention to use GAI in future teaching. At the same time, perceived risk had a negative effect. In addition, performance expectaancy, facilitating conditions, and price value did not significantly influence behavioural intentions, but behavioural intentions and facilitating conditions had significant predictive effects on future use. Based on the findings, this study provides practical recommendations for optimising the user experience of GAI tools, enhancing technical support, and reducing perceived risk.