Generative Artificial Intelligence in Teacher-Driven Personalized Learning for K-12 Education: A Systematic Literature Review

This study aims to synthesize recent findings on the use of Generative Artificial Intelligence (GenAI) in teacher-driven personalized learning in K-12 education. This review uses the Systematic Literature Review (SLR) method with the PRISMA 2020 guidelines, searching the Scopus database for the year 2025. The search strategy employs a combination of keywords covering aspects of GenAI, personalized learning, the K-12 context, and the role of teachers. Out of 319 articles identified, 34 met the inclusion criteria and were analyzed thematically to address three research questions: (1) the forms of GenAI utilization for personalized learning in K-12, (2) the role and strategies of teachers in designing, implementing,and (3) the enabling and hindering factors of implementation. The study findings indicate that GenAI is utilized to generate adaptive learning materials, provide interactive virtual tutors, facilitate adaptive assessments, and create contextual learning simulations. Teachers' roles include learning designers, facilitators, evaluators, and developers of students' AI literacy, with strategies such as prompt engineering, LMS integration, and performance-based assessments. Supporting factors include infrastructure availability, teacher training, clear policies, and platform integration; while barriers include low teacher readiness, digital divides, ethical concerns, and limitations in adapting local content. This study highlights the novelty of a teacher-centered perspective and the synthesis of pedagogical strategies with the potential of GenAI technology, recommending practice-based training, ethical policies, infrastructure equity, and the integration of AI literacy into the curriculum.