Exploring the acceptance of generative artificial intelligence for language learning among EFL postgraduate students: An extended TAM approach

Abstract

AbstractThis study delves into the acceptance of generative artificial intelligence (GenAI) for English language learning among Chinese postgraduate students, examining how individual, social, and technological factors influence this process. Utilizing an extended technology acceptance model, the research collected data from 497 students via a survey, analyzed through partial least square‐structural equation modeling. Key findings underscore personal innovativeness, subjective norms, and trust as significant predictors of GenAI adoption, with an intricate interplay between perceived ease of use and usefulness affecting behavioral intentions. The insights offer theoretical and practical implications for enhancing GenAI's educational integration, emphasizing the importance of fostering innovation, peer influence, trust, and support infrastructure. This contribution enriches the understanding of GenAI's educational potential, particularly in non‐native English contexts, paving the way for further exploration in this evolving domain.