Unraveling EFL students’ technology self-efficacy, emotions and behavioral intentions in leveraging large language models: the serial mediation of perceptions and attitudes

With the growing momentum to integrate artificial intelligence (AI) into education, large language models (LLMs) have shown great potential for English as a Foreign Language (EFL) learning. However, the relationships between EFL students' technology self-efficacy, emotions toward LLM-assisted English learning, and their behavioral intentions remain underexplored. Guided by the Technology Acceptance Model (TAM), this study explores how technology self-efficacy and emotions (i.e., perceived enjoyment and technology anxiety) influence behavioral intentions, with perceptions (i.e., perceived ease of use and perceived usefulness) and attitudes serving as mediating variables among Chinese EFL university students. A total of 677 valid questionnaires were collected and structural equation modeling (SEM) was employed for data analysis. The results revealed that: (a) technology self-efficacy positively predicted perceived enjoyment while negatively predicting technology anxiety; (b) technology self-efficacy positively predicted perceptions, which in turn predicted attitudes and ultimately behavioral intentions; (c) perceived enjoyment positively predicted perceived usefulness, which subsequently predicted attitudes and ultimately behavioral intentions; and (d) technology anxiety negatively predicted perceived ease of use, which in turn predicted attitudes and ultimately behavioral intentions. Overall, this study validated an extended TAM framework in the context of LLM-assisted language learning, showing how cognitive and emotional factors influence behavioral intentions through perceptions and attitudes in a serial manner. These findings offer valuable insights for integrating LLMs into foreign language learning.