Factors associated with Chinese vocational college students’ intention to use generative artificial intelligence for learning: an extended UTAUT mode

Abstract

This study focuses on examining the factors influencing Chinese vocational college students’ behavioral intention to use generative artificial intelligence (GenAI) for learning and further analyzing the relationships between these factors and changes in GenAI behavioral Intention. Based on the Unified Theory of behavioral Intention and Use of Technology (UTAUT), an extended model incorporating anxiety (ANX) was developed, and a two-stage analytical approach combining structural equation modeling (SEM) and ordered Probit (OProbit) regression was adopted. In the first stage, SEM was employed to examine the effects of various factors on behavioral intention within the extended UTAUT model. In the second stage, an OProbit model was applied, with changes in GenAI behavioral Intention specified as an ordinal outcome variable, to further examine the relationships between the relevant factors and changes in behavioral Intention while controlling for individual characteristics, including gender, grade level, and prior AI usage experience. The results indicate that effort expectancy (EE), social influence (SI), and facilitating conditions (FC) are significantly and positively associated with vocational college students’ behavioral intention to use GenAI for learning, whereas ANX is significantly and negatively associated with behavioral intention. In addition, GenAI behavioral Intention varies to some extent across student groups with different genders, grade levels, and levels of AI usage experience. This study provides empirical evidence for the application of GenAI in vocational education and offers practical implications for optimizing GenAI-based teaching and adoption strategies, enhancing students’ engagement in learning, and improving the quality of talent development in vocational education.