Applications, multidimensional challenges, and innovative pedagogical practices of generative AI in mathematics education: a systematic review 2022–2026

Abstract

This study investigates the application of generative artificial intelligence (generative AI) in mathematics education from 2022 to 2026. Drawing on a systematic literature review of 268 articles retrieved from OpenAlex, and EBSCO databases, we identify and analyze 66 core research articles through a rigorous screening process following PRISMA guidelines. The findings reveal three key patterns. First, generative AI applications in mathematics education span four domains: student learning support (64%), teacher instruction assistance (38%), assessment and feedback innovation (27%), and special education (2%), with student learning support as the dominant application area. Second, while generative AI shows positive effects in enhancing learning motivation and providing personalized support, it exhibits notable limitations in mathematical reasoning accuracy and critical thinking development—with 70% of studies documenting AI limitations. Third, challenges associated with these applications are multifaceted, encompassing technical (30%), pedagogical (52%), ethical (20%), and implementation (42%) dimensions. We propose an integrated analytical framework combining the Technological Pedagogical Content Knowledge (TPACK) framework with hybrid intelligence theory, and identify a novel pedagogical paradigm in which AI errors serve as instructional resources for cultivating critical thinking. Future research directions include broadening application domains, deepening theoretical frameworks, and strengthening empirical investigation. This review is limited to English-language publications from two databases, with the search closing in June 2026. Despite these limitations, the review contributes systematic evidence and practical guidance for the effective integration of generative AI in mathematics education.