Understanding pre-service teachers' acceptance of generative artificial intelligence: an extended technology acceptance model approach

Generative Artificial Intelligence (GAI) has received widespread attention recently, influencing teacher education in various ways. However, there is little discussion on pre-service teachers' behavioral intention towards GAI. Therefore, this study employs subjective norm, AI self-efficacy, facilitating conditions, and trust to expand the Technology Acceptance Model, understanding pre-service teachers' adoption of GAI. The study involves 486 undergraduates from a university in Jiangsu Province, China. The Partial Least Squares Structural Equation Model is used to test the research model. Research model proved to be both reliable and valid, confirming nine out of ten hypotheses. The findings indicate that: (1) AI self-efficacy strongly predicts perceived ease of use; (2) The most direct and strongest impact on perceived usefulness is perceived ease of use, followed by facilitating conditions; (3) Perceived ease of use doesn't directly affect attitude towards use, but perceived usefulness and trust significantly influence this attitude; (4) Attitude towards use greatly predicts behavioral intention, followed by perceived usefulness and subjective norm. This research helps in advancing policy development in educational institutions and the integration of GAI and teacher education.