Research on the Influencing Factors of Teachers' Willingness to Use Generative Artificial Intelligence in Teaching Practice of vocational colleges and schools in China

Abstract The rapid development of GAI technology has increasingly highlighted its potential applications in the education sector. As vocational institutions serve as critical platforms for cultivating technical and skilled professionals, faculty adoption intentions toward GAI directly determine the effectiveness of technology-enhanced education. To address these gaps, this study integrates the UTAUT2 theoretical framework by incorporating two key variables - academic pressure and attitude - to construct a comprehensive model examining factors influencing GAI adoption among vocational college faculty. Employing a hybrid analytical approach combining SEM and ANN analysis, we systematically investigate the pathways through which core variables (performance expectancy, effort expectancy, price value, social influence, and habit) affect behavioral intentions, while elucidating the mediating role of attitude. The findings demonstrate that these factors collectively exert significant positive effects on GAI adoption intentions, with academic pressure exhibiting differential impacts across various faculty subgroups. This study not only extends the theoretical applicability of UTAUT2 to vocational education contexts but also provides empirical evidence to inform targeted GAI promotion strategies in vocational institutions. Future implementations should incorporate both individual faculty characteristics and institutional policies to design differentiated training and incentive mechanisms, thereby enhancing the feasibility and effectiveness of GAI technology integration.