Exploring EFL learners’ behavioral intention toward AI-based conversational tools through the lens of UTAUT and Self-Determination Theory

AI-based conversational tools have attracted increasing attention in EFL speaking practice because they offer real-time interaction and flexible opportunities for oral communication, yet learners' intention to use these tools remains insufficiently understood. Drawing primarily on the Unified Theory of Acceptance and Use of Technology, with Self-Determination Theory as a complementary perspective, this study examines factors associated with EFL learners' behavioral intention to use AI-based conversational tools. A cross-sectional survey was conducted among 568 Chinese undergraduate EFL majors, most of whom were early-stage, low-frequency users of AI-based conversational tools. Structural equation modeling showed that behavioral intention was mainly associated with performance expectancy, hedonic motivation, and effort expectancy, while social influence and facilitating conditions showed weaker associations. The analysis identified significant indirect associations involving autonomy or competence and intrinsic motivation, whereas the corresponding association involving relatedness was not significant. Taken together, these findings suggest that learners' intention to use AI-based conversational tools is related both to how they evaluate the technology and to the motivational quality of the speaking practice it supports. In practice, teachers should organize purposeful speaking activities that offer meaningful learner choice and make progress visible, while developers should ensure that AI-based conversational tools are useful, enjoyable, and easy to use.