What Matters Most? A Q‐Method Study on Chinese University Students' Perceived Teacher Support in AI‐Assisted EFL Learning

ABSTRACT With the rapid integration of artificial intelligence (AI) into English as a Foreign Language (EFL) classrooms, understanding how human teachers can effectively support learners has become increasingly important. This study investigates Chinese university students' perceived importance of different forms of teacher support in AI‐assisted EFL learning through Q methodology. A Q‐set instrument consisting of 32 statements across five dimensions of teacher support (i.e., affective, pedagogical, technical, autonomy, and feedback) was developed for Q‐sorting, and complemented by semi‐structured interviews. Analysis of 32 participants' Q‐sorts revealed three distinct learner profiles: High Structure Learners, Comfort‐Seeking Learners, and Practice‐Driven Learners. These groups demonstrated heterogeneous preferences, with some prioritising structured pedagogical scaffolding, others emphasising emotional reassurance, and still others focusing on hands‐on technical support and regular practice. The findings underscore the multidimensional and learner‐specific nature of teacher support in AI‐assisted EFL classrooms and emphasise the importance of tailoring teacher roles to meet diverse student needs in technology‐enhanced learning.