Task-specific emotions and EFL learners’ technology acceptance beliefs in AI-mediated English speaking

Abstract

This study investigates how English as a Foreign Language (EFL) learners’ beliefs about technology acceptance predict their task-specific enjoyment and boredom during an AI-mediated speaking practice course. Specifically, it examines how perceptions of ease of use and usefulness influence both general and specific aspects of enjoyment (including task characteristics, personal enjoyment, and social interactions) and boredom (including task characteristics, personal boredom, and social interactions) in language learning. Data were collected from 141 Iranian EFL learners participating in an online speaking class. To enhance the precision and accuracy of the analyses, preliminary procedures identified the optimal measurement structure for enjoyment and boredom, leading to the adoption of a bifactor exploratory structural equation modeling (bifactor-ESEM) representation. The study then used a structural model to validate the relationships among learners’ perceptions and their emotional experiences. The findings highlight the predictive role of technology acceptance beliefs in shaping both general and specific facets of enjoyment and boredom, with global task-specific enjoyment negatively associated with global task-specific boredom. These results may inform the design of emotionally supportive Intelligent Computer-Assisted Language Learning (ICALL) activities tailored to the tasks’ unique demands and provide deeper insights into learners’ experiences and emotional well-being.