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https://doi.org/10.62836/jissh.v3i3.0032
Lin, Z., & Xu, X. (2026). Construction of Generative-AI-Enabled Educational Pathways for Extracurricular Foreign Language Learning Under Ideological and Political Guidance: An Exploration of the “Student-AI-Teacher” Tripartite Collaborative Model in Discontinuous-Data Scenarios. Journal of Integrated Social Sciences and Humanities, 3(3), 0032. https://doi.org/10.62836/jissh.v3i3.0032
Volume 3, Issue 3, 2026

Construction of Generative-AI-Enabled Educational Pathways for Extracurricular Foreign Language Learning Under Ideological and Political Guidance: An Exploration of the “Student-AI-Teacher” Tripartite Collaborative Model in Discontinuous-Data Scenarios

Against the backdrop of the in-depth integration of the digital transformation of education and curriculum-based ideological and political education, College English teaching remains plagued by such challenges as students’ inadequate lexical competence, the lack of personalized learning resources, and insufficient motivation for autonomous learning. Meanwhile, existing AI-empowered educational research has paid markedly insufficient attention to the scenario of discontinuous after-class learning data. In view of this, this study takes 21 non-English major undergraduates enrolled in 2023 from Guangdong University of Foreign Studies South China Business College as the research sample, and constructs a tripartite collaborative model of “student-led autonomous generation, AI-enabled precise empowerment, and teacher-targeted guidance” through an 8-week teaching experiment. A mixed analytical framework integrating frequency-weighted analysis, individual longitudinal tracking, and extraction of common issues is adopted for empirical verification and in-depth exploration. The results indicate that the proposed model can significantly improve students’ cloze problem-solving competence, lexical application competence, and autonomous learning awareness, and “4 effective practice sessions” is identified as the critical threshold for its synergistic effect. Meanwhile, this study reveals the behavioral characteristics of students’ autonomous learning and the potential risks of AI application in the context of discontinuous data, and establishes a full-process integration path for curriculum-based ideological and political education. This research not only provides a replicable paradigm for after-class College English teaching in application-oriented undergraduate institutions, but also points out a clear direction for the coordinated development of technology and value in foreign language teaching in the AI era.

generative AI college English autonomous learning curriculum-based ideological and political education after-class teaching

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Supporting Agencies

  1. Funding: This research was funded by the 2024 Curriculum‑based Ideological‑Political Demonstration Course Project of South China Business College Guangdong University of Foreign Studies, Grant No.  2024KCSZ09.