Generative Artificial Intelligence in Supporting the Transformation of Mathematics Education: An Analysis of Research Trends, Opportunities, Challenges, and Directions for Development

Abstract

The transformation of mathematics education in the digital age still faces various challenges, such as limitations in personalized learning, the provision of adaptive feedback, educators’ readiness to integrate technology, and the lack of a comprehensive synthesis on the development of Generative Artificial Intelligence (GenAI) applications in this field. Therefore, this study aims to analyze research trends, opportunities, challenges, and the direction of GenAI development in supporting the transformation of mathematics education. The study employed a Systematic Literature Review (SLR) method following the PRISMA guidelines. The systematic literature search was conducted through Google Scholar, Scopus, the Directory of Open Access Journals (DOAJ), Elicit, Scite.ai, and SciSpace. A total of 39 studies met the inclusion criteria and were included in the final literature synthesis. The results indicate that research on Generative Artificial Intelligence (GenAI) has seen a significant increase and has shifted from a technology-oriented focus to a pedagogical one. Six main research areas were identified: the use of GenAI in mathematics education (5 studies), pedagogical implementation and teacher support (6 studies), the impact of implementation (8 studies), challenges and ethical issues (8 studies), integration strategies (5 studies), and future research directions (7 studies). GenAI has the potential to enhance personalized learning, adaptive feedback, and learning efficiency; however, its implementation still faces challenges related to ethics, accuracy, teacher readiness, and digital infrastructure. The findings of this study provide a conceptual foundation as well as recommendations for developing pedagogical, ethical, and sustainable GenAI integration strategies in the transformation of mathematics education.