Generative AI in Pre-Service Science Teacher Education: A Systematic Review

Despite the increasing adoption of Generative Artificial Intelligence (GenAI) in education, there is a lack of comprehensive reviews on how GenAI is being utilized within Pre-Service Teachers (PSTs) in science. This systematic literature review (SLR) aims to address this gap by examining the extent and nature of GenAI integration in future science teachers' preparation programs. Using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, 21 peer-reviewed empirical studies published between 2022 and 2025 were identified and analyzed through qualitative thematic synthesis. The analysis addresses three research questions: 1) the extent to which GenAI is used in the curriculum of PSTs in science education; 2) how PSTs use GenAI tools to develop a deeper understanding of science and develop scientific reasoning; and 3) how PSTs in science education are using GenAI tools to plan and carry out teaching activities. Findings reveal that the integration of GenAI into curricula remains fragmented and often experimental, typically confined to technology-related courses or pilot projects. PSTs primarily utilize GenAI tools for conceptual clarification, hypothesis generation, and self-regulated learning. Furthermore, these tools serve as cognitive partners in designing lesson plans, differentiating instruction, and simulating classroom scenarios. However, the absence of structured pedagogical guidance often leads to superficial use and limited critical evaluation of AI-generated content. This review highlights the transformative potential of GenAI in science education while underscoring the need for institutional frameworks, faculty training, and the development of AI literacy. Future research should focus on how to sustainably integrate GenAI into teacher education to foster scientific reasoning, pedagogical adaptability, and responsible use of technology.