GENERATIVE ARTIFICIAL INTELLIGENCE IN MATHEMATICS EDUCATION: A SYSTEMATIC REVIEW OF DATA-DRIVEN APPLICATIONS, LEARNING THEORIES, AND IMPLICATIONS FOR SUSTAINABLE DEVELOPMENT GOAL - Khoirul Anwar; Adelia Sherlyna; Alyssa Marfinda Salsanifa; Havi Ayuning Tyas; Syahda Eka Prayudistyan; Genta Aldi Saputra; Iwan Maulana (LPPM Institut Ahmad Dahlan Probolinggo, 2026)
The rapid advancement of generative artificial intelligence (AI) has introduced transformative opportunities in mathematics education, yet its implications for pedagogical practices, learning theories, and sustainable development remain underexplored. This systematic review examines the intersection of generative AI and mathematics education, focusing on data-driven applications, theoretical frameworks, and their alignment with Sustainable Development Goal 4 (SDG 4), which advocates for inclusive and equitable quality education. We synthesize existing research to identify key trends, challenges, and opportunities across multiple dimensions, including higher education, STEM disciplines, adaptive learning, and ethical considerations. By analyzing diverse scholarly works, we uncover how gesnerative AI supports personalized learning, enhances problem-solving skills, and fosters engagement while addressing disparities in educational access. The review highlights the role of generative AI in promoting active learning through interactive tools, yet it also reveals concerns regarding algorithmic bias, data privacy, and the need for teacher preparedness. Our findings suggest that while generative AI holds significant potential to democratize mathematics education, its responsible integration requires robust pedagogical strategies and policy frameworks. The study contributes to ongoing discussions on AI-driven educational innovation by offering evidence-based insights for researchers, educators, and policymakers aiming to harness generative AI for sustainable educational development.