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As generative artificial intelligence (Gen AI) rapidly transforms educational practices and reshapes workforce skill demands, vocational education faces unique challenges in adapting to these changes. Understanding vocational teachers’ perceptions and adoption of Gen AI is thus essential for ensuring effective integration of emerging technologies into teaching. The current study explored the factors influencing vocational teachers’ adoption of Gen AI in China, with a focus on disciplinary and regional differences. Structural equation modeling (SEM) was conducted on survey data from 1028 teachers. Results indicated that perceived usefulness, perceived easy of use, social influence, and facilitating conditions significantly shaped behavioral intention and actual usage. Multi-group SEM analysis revealed disciplinary differences: Humanities and Social Sciences teachers were more influenced by social norms, whereas Natural Sciences teachers were more responsive to perceived usefulness and easy of use. No significant regional differences were observed. These findings provide valuable insights for policymakers and institutions aiming to support diverse teacher groups in integrating Gen AI into vocational education.
Cui et al. (Thu,) studied this question.