Pre-service Teachers’ Intention to Use Artificial Intelligence Research during 2005–2025: A Review Based on Bibliometric Analysis

Abstract

This study offers a comprehensive bibliometric overview of research on pre service teachers’ intention to adopt artificial intelligence (AI) in education from 2005 to 2025. Using the Web of Science Core Collection, 522 records were initially retrieved and 477 peer reviewed articles were retained after PRISMA aligned screening. CiteSpace (LLR clustering, burst detection, co citation networks) and VOSviewer/RAWGraphs were employed to map publication trends, intellectual structures, and collaborative patterns. Results show three temporal waves: (1) 2004–2010 foundations in TAM, UTAUT, and TPB; (2) 2011–2017 emphasis on digital competence, self efficacy, and teacher readiness; and (3) 2018–2025 acceleration of AI specific themes—ethics, trust, explainability, and pedagogical innovation. Core clusters revolve around technology acceptance constructs (perceived usefulness/ease, attitude, intention), digital competence frameworks, and AI enabled instructional design. Author and source co citation analyses highlight enduring methodological anchors (e.g., PLS SEM standards) and the dominance of journals such as Computers & Education, Computers in Human Behavior, and the emergent Computers and Education: Artificial Intelligence. Geographically, China, the United States, and several European countries lead in productivity and centrality, reflecting an increasingly interdisciplinary and collaborative landscape. Practically, findings point to the necessity of embedding holistic AI literacy—technical, pedagogical, and ethical—into teacher education, while addressing affective barriers like anxiety. Limitations include the single database scope and the quantitative nature of bibliometrics; future research should triangulate with qualitative syntheses, track policy impacts longitudinally, and examine equity dimensions (gender, region, institutional type) in AI adoption.