Unpacking the Chain From Interaction to Intention: The Serial Mediation of AI Learning Interest and Trust in Linking Teacher–Student Interaction to Willingness to Communicate With AI

This study investigates the mechanisms through which teacher–student interaction influences university students' willingness to communicate with AI in AI‐assisted English learning environments, with particular attention to the serial mediating roles of AI learning interest and AI trust. Grounded in Social Cognitive Theory and the Technology Acceptance Model, the study surveyed 443 university students from four higher education institutions in eastern China. Results revealed that AI learning interest and AI trust served as significant mediators between teacher–student interaction and willingness to communicate with AI, and that the sequential pathway from teacher–student interaction through AI learning interest to AI trust and finally to willingness to communicate with AI was statistically significant. This serial mediation pathway suggests that environmental factors shape affective internal states, which in turn promote cognitive evaluations and ultimately drive behavioural intentions. The study extends research on teacher–student interaction to AI‐supported language learning contexts and integrates interpersonal interaction with technological acceptance into a unified explanatory framework. Practically, these findings suggest that language educators should prioritize cultivating students' AI learning interest through meaningful classroom interactions and design instructional activities that progressively build trust in AI technologies. Meanwhile, AI developers may consider incorporating interactive features that facilitate teacher–student dialogue and scaffold learners' willingness to communicate with AI.