: 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.
Sha Li, Yuheng Sun· Advances in Educational Tech...· 0 citations
: The growing use of generative artificial intelligence in Comprehensive English instruction has been accompanied by a tendency to treat AI-assisted preparation, drafting, and revision as evidence of improved learning. This paper examines that assumption by asking how AI-supported materials can be incorporated into a course whose central work remains close reading, language practice, and the formation of students’ own understanding. Drawing on constructivist learning theory and distributed cognition, the paper proposes a teacher-student-AI coordination model for interactive-generative teaching. The model identifies three conditions under which AI support can become pedagogically meaningful. Teachers need to make course goals and tool-use boundaries explicit through task design. Students need to explain and revise their understanding in ways that make meaning construction visible. The intelligent system needs to preserve questions, drafts, feedback, and revision traces for later judgment. From this perspective, AI-assisted learning in Comprehensive English shows practical value in extending resources, supporting feedback, and recording the learning process, yet its value depends on how these functions are brought back into text-based tasks, classroom interaction, and teacher assessment. These distinctions help clarify the responsibilities of teachers, students, and intelligent systems in AI-mediated language learning and provide a course-level pathway for redesigning content, interaction, process, and assessment in Comprehensive English.
Sha Li, Yuheng Sun· Frontiers in Educational Res...· 0 citations
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