Generative AI in teacher education: a systematic review

Abstract

This study addresses a critical gap in the literature by conducting one of the earliest systematic reviews (2021-2025) on generative artificial intelligence (GenAI) in teacher education. Using a structured screening and coding process, 35 peer-reviewed articles from Scopus and Web of Science (WoS) were analyzed to examine methodological trends, geographical disparities, and cross-cultural adaptability. The review identifies four major application areas, including stakeholder perception analysis, instructional resource generation, curriculum design, and student-AI collaborative learning, and synthesizes their underlying pedagogical mechanisms. Key findings reveal pronounced geographical imbalance (with no studies from Africa or Latin America), heavy reliance on short-term qualitative designs, and limited empirical or longitudinal validation. Based on these insights, the study proposes a conceptual framework linking GenAI applications, challenges, and future research pathways. This work contributes a structured evidence base and offers guidance for advancing GenAI-integrated teacher education through more rigorous, inclusive, and context-sensitive research.