The core focus and effect of integrating generative artificial intelligence into the reform of ideological and political general education teaching: a case study of China based on constructivist learning theory

Teaching reform is a key driver of paradigm shifts in education. A key question when integrating artificial intelligence(AI) into ideological and political general education courses (IPGEC) is which reform orientation to pursue. Using a case study approach and drawing on the analytical framework of constructivist learning theory(CLT), this study collected data from 12 teachers and 201 students across six classes. Four representative teachers and six students were selected for in-depth interviews, which were coded and analyzed. Triangulation was achieved through classroom observations, interviews, and feedback on assignments. The findings show that each of the four reform orientations improves learning outcomes but in different ways. Technology development orientation enhances students' ability to transfer technical skills but also increases the burden of learning AI. Interest accumulation orientation has a more lasting impact; however, it requires continuously finding new sources of interest to maintain motivation. Thinking-development orientation plays an important role in strengthening critical thinking, but it requires teachers to guide students and keep the content appropriate for university-level students. Knowledge-accumulation orientation helps build a knowledge system, although it adds to the workload of knowledge acquisition. Therefore, we recommend that universities allow teachers to explore different orientations of AI-integrated teaching reform, develop model practices, and gradually extend these practices to other courses.