Factors Affecting Vocational Teachers’ Adoption of Generative AI: A Multigroup Analysis by Discipline and Region

• In Event: Emerging Considerations for Designing In-Service Teacher Training in Generative AI

Abstract

The current study explored the factors influencing vocational teachers’ adoption of generative AI in China, with a focus on disciplinary and regional differences. Structural equation modeling (SEM) was conducted on data collected from 1028 teachers. Results indicated that perceived usefulness, perceived easy of use, social influence, and facilitating condition played significant roles in shaping behavioral intention and actual usage. Multigroup 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 offered valuable insights for policymakers aiming to support diverse teacher groups in the integration of generative AI into teaching practices.

Authors

• Yiran Cui, Shandong University

• Meng Li, China National Academy of Educational Sciences