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, Yuhe Sun. AI-Empowered Interactive-Generative Teaching in Comprehensive English: A Teacher-Student-AI Coordination Model. Frontiers in Educational Research (2026), Vol. 9, Issue 7: 92-101. https://doi.org/10.25236/FER.2026.090711.

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