Systematic Review on Teacher Education and the Integration of Artificial Intelligence

Abstract

The rapid expansion of Generative Artificial Intelligence (GenAI), AIGC tools, and digital simulations is reshaping teacher education. However, research on how these technologies is integrated into pre-service and in-service teacher training remains fragmented, with limited synthesis across pedagogical, psychological, and institutional dimensions. This systematic review synthesizes empirical evidence (2025) on the integration of GenAI and digital simulations in teacher training programmes, examining their effects on pedagogical competencies, digital readiness, and professional identity formation. The bibliographic coupling results show that the included studies cluster into four main intellectual groups. The strongest cluster focuses on teaching and learning foundations, reflecting shared pedagogical references. A second cluster centres on teacher education and professional development, linked through TPACK, readiness, and adoption models. A third cluster connects curriculum and subject-specific research, while a fourth highlights technology-driven studies on AI, VR/AR, and digital learning environments. Together, these clusters illustrate both a common theoretical base and growing diversification in AI-related teacher education research. GenAI emerged as a pedagogical partner that enhances lesson planning, differentiation, feedback, and teacher self-directed professional development. Digital simulations, including VR, avatar-based environments, and 3D modelling, supported communication skills, design thinking, and scenario-based decision-making. Across studies, teachers displayed curiosity and optimism but lacked comprehensive AI literacy, with readiness shaped by TPACK profiles, digital-competence frameworks, TAM and TPB constructs, and evolving professional identities. Key barriers included infrastructure inequality, insufficient training, overreliance on self-report outcomes, and concerns about creativity, authenticity, and ethical use. Teacher education programmes must embed structured AI-literacy curricula, model AI-rich instruction, ensure equitable access to technological infrastructure, and strengthen institutional support systems. Policy frameworks should incorporate ethical guidelines, assessment standards, and mechanisms for sustained professional development. AI-enabled teacher training holds substantial potential to enhance pedagogical competence and reshape professional identity. Yet its effectiveness depends on long-term integration, rigorous evaluation, and equitable implementation. Coordinated curricular, institutional, and policy reforms are essential for translating AI innovation into meaningful and sustainable teacher development.