A Bibliometric Analysis of AI-supported Teacher Education

Tianru Zhang (Academy of Future Education, Xi’an Jiaotong-Liverpool University, Suzhou, China)
Wang Li (Academy of Future Education, Xi’an Jiaotong-Liverpool University, Suzhou, China)
Na Li (Academy of Future Education, Xi’an Jiaotong-Liverpool University, Suzhou, China)
Stuart Thomason (Department of Computer Science, University of Liverpool, Liverpool, United Kingdom)
Xiaojun Zhang (Academy of Future Education, Xi’an Jiaotong-Liverpool University, Suzhou, China)

Acta Pedagogia Asiana
Volume 5 - Issue SI - 2026

Teacher education has undergone substantial transformation in recent decades, particularly with the rapid integration of artificial intelligence (AI)–driven educational technologies into training and professional development programs. These advancements have reshaped pedagogical approaches, instructional design, and the overall structure of teacher preparation. To provide a comprehensive understanding of the scientific development in this domain, this study systematically examines the application of AI in teacher education over the past 25 years. A total of 107 peer-reviewed articles were carefully screened and selected from two major academic databases, Scopus and the Web of Science (WoS) Core Collection. Using bibliometric analysis, this study identifies key publication trends, influential authors, collaborative networks, and emerging research themes. The findings reveal a consistent and significant increase in scholarly attention toward AI-assisted teacher education, particularly in the last decade. Moreover, the impact of AI is no longer confined to pre-service teacher training but has expanded to support continuous, lifelong professional development. AI technologies such as intelligent tutoring systems, adaptive learning platforms, and data-driven decision-making tools, are increasingly being utilized to enhance teachers’ instructional competencies, reflective practices, and personalized learning pathways. Importantly, the analysis highlights a recent surge in the adoption of generative AI within teacher education, especially over the past two years. This development signals a paradigm shift, where AI is not only used as a supportive tool but also as a co-creator of educational content and pedagogical strategies. As a result, teacher training models appear to be entering a new phase characterized by innovation, personalization, and increased reliance on human–AI collaboration. Overall, this study provides valuable insights into the evolving landscape of AI in teacher education and underscores its growing significance in shaping the future of teaching and learning.