Impact of AI chatbots on EFL learners’ technology adoption

Abstract This study investigates the factors influencing English as a Foreign Language (EFL) learners’ adoption of AI chatbots by extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. The research incorporates two additional constructs, Core Self-Evaluation (CSE) and Learning Value (LV), to enhance the model’s predictive power in the context of language learning technology. A quantitative approach was employed, collecting data from 362 English-major undergraduates at a prominent university, using structured survey questionnaires. The data were analyzed using partial least squares structural equation modeling (PLS-SEM) to evaluate the relationships within the augmented UTAUT2 model. The results reveal that performance expectancy, facilitating conditions, habit, CSE, and LV significantly influence EFL learners’ behavioral intentions and actual use of AI chatbots. Effort expectancy, social influence, and hedonic motivation were found to have no significant impact on adoption intentions. These findings underscore the importance of aligning AI chatbot functionalities with learners’ educational goals and supporting their self-evaluative beliefs to promote technology acceptance in language learning. The study advances the UTAUT2 model by demonstrating the relevance of CSE and LV in predicting EFL learners’ adoption of AI chatbots. The findings offer insights for educators and developers to enhance chatbot design, meeting learners’ pedagogical needs and expectations.