SMART TEACHING IN INTERNATIONAL CHINESE EDUCATION: A CASE STUDY ON THE USE OF GENERATIVE AI - Dong, Xiao; Aijing, Chen (Proceeding of International Conference on Social Science and Humanity, 2025)
Objective: In the context of accelerating global educational digitalization, international Chinese education is facing both new opportunities and structural challenges. Among emerging technologies, Generative Artificial Intelligence (AI) is reshaping the dynamics of teaching design, learning interaction, and cultural transmission. This study aims to examine the theoretical implications of integrating generative AI into international Chinese language instruction, emphasizing its role not only as a pedagogical tool but also as a cognitive and cultural mediator. Method: Utilizing a qualitative, theory-driven methodology, the research constructs a conceptual framework based on Actor-Network Theory, Inter-subjectivity, and the theory of productive forces. The paper proposes a “technology–culture–education” triadic model to analyze how generative AI redefines teacher-student relationships, cross-cultural dialogues, and knowledge production. Results: Findings suggest that generative AI can personalize learning, enhance linguistic immersion, and promote educational equity across borders, while also challenging traditional power dynamics in classroom discourse and pedagogical agency. Novelty: This work contributes to the theoretical foundation for reimagining smart pedagogy in the field of international Chinese education.