Abstract As AI becomes increasingly integrated into language education, teachers' responses to AI‐integrated teaching deserve closer attention. Guided by Self‐Determination Theory, this study employed an explanatory sequential mixed methods design to examine how Chinese university EFL teachers' basic psychological need profiles were associated with their AI use, perceived school support, and perceived professional roles. In the quantitative phase, survey data from 408 teachers were analyzed through latent profile analysis, identifying three profiles: “Motivation‐Constrained”, “Autonomy‐Driven”, and “Relationally Anchored”. In the qualitative phase, interviews with nine teachers selected from these profiles were used to explain profile‐specific patterns. Autonomy‐Driven teachers reported the highest levels of AI use and perceived school support and positioned themselves as instructional designers. Motivation‐Constrained teachers showed low engagement, uncertainty, and limited role adjustment. Relationally Anchored teachers reported less intensive AI use than Autonomy‐Driven teachers but demonstrated ethical caution and a human‐centered orientation. The profile‐based meta‐inference suggests that lower AI use should not be interpreted uniformly as resistance or lack of readiness, but as reflecting distinct configurations of need satisfaction, perceived school support, and perceived professional roles. Findings highlight the value of mixed methods research for TESOL studies of AI integration and the design of profile‐sensitive institutional support.
Jie Yang (Tue,) studied this question.