"Teacher-AI-Student" Tripartite Collaboration: Reconstruction and Empirical Validation of the Teaching Model for Ideological and Political Courses in Higher Education

Abstract

With the advancement of artificial intelligence (AI), its deep integration into ideological and political theory courses (IPTPCs) in higher education brings not only opportunities for the reconstruction of teaching forms but also such problems as weakened value guidance by teachers, the absence of students' subjectivity, and the superficial application of technology. This paper proposes a "teacher–AI–student" tripartite collaborative analysis framework and constructs a human–AI collaborative teaching model for IPTPCs, featuring precise pre-class diagnosis, in-class value guidance, and personalized post-class feedback. Taking Guizhou Qiannan University of Science and Technology as a case, the model is validated through quasi-experimental research, questionnaire surveys, interviews, and platform data analysis. The findings show that the tripartite collaborative model helps enhance students' classroom participation, learning outcomes, and value identification, and also improves the precision of teachers' instruction. However, its effectiveness depends on teachers' digital literacy, platform adaptability, and ethical norms. This research may serve as a model reference and offer practical implications for the digital transformation of IPTPCs in higher education.