Learning Motivation in Generative AI-Supported Comprehensive English Teaching: A Self-Determination Theory Perspective on Teacher-Student-AI Interaction

Abstract

: The entry of generative artificial intelligence into college English classrooms has changed the way students obtain resources, revise their language output, and participate in classroom interaction. While existing studies have mainly emphasized tool functions and teaching efficiency, less attention has been paid to how learning motivation is supported and weakened in this new instructional structure. Drawing on Self-Determination Theory, this paper examines learning motivation in generative AI-supported college English classrooms from the perspective of three-way interaction among the teacher, the student, and the intelligent system. It argues that AI can support motivation when system suggestions create opportunities for autonomous judgment, feedback and revision help students confirm their competence, and human-machine practice returns to authentic classroom communication. At the same time, algorithmic dependence, instant answers, and technological mediation may weaken autonomy, reduce the experience of competence, and undermine relatedness. The paper further proposes practical adjustments from both teacher support and student autonomy, emphasizing that system output should become learning material only after pedagogical judgment, critical processing, and interpersonal verification. This analysis clarifies the motivational conditions and risk boundaries of AI-supported college English teaching.