A Bibliometric Analysis of Artificial Intelligence Integration in Teacher Education in China

This thesis presents a bibliometric analysis of the integration of Artificial Intelligence (AI) in teacher education, utilizing CiteSpace software to explore academic publications in the China National Knowledge Infrastructure (CNKI) which is the most influential academic database in China. With the rapid development of AI technologies and their potential impact on education, this study aims to systematically map the evolution of AI in teacher education research within the Chinese academic context. By analyzing a comprehensive dataset of published articles, the study identifies key trends, research hotspots, and emerging themes in the field. The bibliometric indicators, such as publication frequency, citation patterns, co-authorship networks, and keyword co-occurrence, are examined to reveal the scholarly landscape and to track the growth of AI -related educational research. The findings highlight the major contributors to the field, the development trajectory of AI in teacher training, and the collaboration networks that shape this growing domain. Additionally, this research offers insights into the challenges and opportunities that AI presents to teacher education in China, suggesting directions for future research and policy development. Overall, the study underscores the importance of AI integration in shaping the future of teacher education and provides a framework for understanding its impact through bibliometric analysis.