Toward an Integrated Framework for Understanding and Guiding Human-AI Collaboration in Secondary School EFL Teaching

Siyuan Yang; Baohua Su; Xixi Yang

doi:10.61414/y569v827

Abstract

This study explores the impact of human-AI collaborative teaching strategies on English teachers in secondary schools. Based on semi-structured interviews with five English teachers in Jiangxi Province, thematic analysis was conducted using the SAMR, UTAUT, and GHEX-IPACK theoretical frameworks. The findings indicate that AI technology is primarily applied in scenarios such as resource generation, assignment distribution, and learning analytics. By substituting traditional tools, enhancing teaching interactions, and reconstructing instructional processes, AI facilitates a shift in teaching strategies from “teacher-led” to “human-AI collaboration”. Teachers generally recognized the potential of this model for improving efficiency and supporting personalized learning, but also pointed out challenges, including data bias, hardware limitations, and a lack of emotional interaction. The study suggests that achieving deep human-AI collaboration requires balancing technological efficacy with humanistic care relying on blended instructional design and teacher training to optimize teachers’ knowledge structures. This research preliminary constructs a practical model of human-AI collaboration in secondary school English education, providing insights for teacher professional development.

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