Mapping the research landscape of generative artificial intelligence in education with implications for teacher education: A bibliometric analysis
The rapid emergence of generative artificial intelligence (GenAI), particularly large language models, has significantly transformed educational research and practice, resulting in a rapidly expanding yet fragmented body of literature. This study aims to systematically map the research landscape of GenAI in education and examine its implications for teacher education through a comprehensive bibliometric analysis. Using the Scopus database, 16,250 documents published from 2021 to 2025 were analyzed through performance analysis and science mapping techniques, including keyword co-occurrence, co-authorship network analysis, and citation analysis to capture the structural and intellectual development of the field. The findings reveal a sharp increase in publication output beginning in 2023, indicating a transition from an emergent to a high-growth phase of research development. Research productivity and scholarly influence are concentrated among a limited group of authors, institutions, and countries, particularly the United States and China, reflecting uneven global participation. Collaboration networks exhibit a multi-clustered structure, suggesting active yet fragmented research communities with limited cross-group integration. Citation patterns indicate that a small number of recent publications exert strong influence and serve as foundational references in the field. Thematic analysis further identifies three dominant and interconnected areas: core technological developments, educational applications, and domain-specific implementations, highlighting the interdisciplinary nature of GenAI in education. The field is expanding rapidly but remains structurally fragmented and is still undergoing intellectual consolidation. These findings emphasize the need for more cohesive research efforts, stronger international collaboration, and greater integration across thematic domains. Implications for teacher education include the integration of AI-related competencies into pre-service programs, the adoption of pedagogically grounded and context-sensitive approaches, and the provision of continuous professional development to support teachers in AI-enhanced learning environments.