Factors Driving AI Adoption in English Language Learning: An Extended UTAUT2 Analysis of Vietnamese Students

The rapid advancement of Artificial Intelligence (AI) has transformed language education, yet limited research has explored the determinants of AI adoption among English learners in developing countries. This study investigates the key factors influencing Vietnamese students’ behavioral intention and actual use of AI-powered applications in their English learning process. Drawing on an extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework, the research model incorporates seven predictors — Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Hedonic Motivation, Price Value, and Habit — to explain both Behavioral Intention and Use Behavior. A self-administered questionnaire was distributed to 184 Vietnamese students with prior experience using AI tools for English learning, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that only three factors significantly predict Use Behavior: Behavioral Intention, Facilitating Conditions, and Habit, with Behavioral Intention and Facilitating Conditions emerging as the strongest drivers. Furthermore, the study extends the existing literature by examining the combined influence of intrinsic and extrinsic personal factors on AI adoption through the lens of perceived value. These results offer practical implications for educators, edtech developers, and policymakers seeking to promote effective AI integration in English language education within the Vietnamese context.