Artificial Intelligence and Universal Design for Learning to Strengthen Problem-Solving and Inclusive Participation in Mathematics Teaching at the Basic and Secondary Education Levels.
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The integration of artificial intelligence (AI) into mathematics education offers new opportunities for personalized learning, feedback, and support during problem-solving. However, its contribution to genuinely inclusive education depends on pedagogical approaches that recognize and respond to learner diversity. This study systematically reviewed and analyzed the scientific evidence on AI and Universal Design for Learning (UDL) in mathematics education at the primary and secondary school levels, with particular attention to problem-solving and inclusive participation. The systematic review was conducted and reported in accordance with PRISMA 2020 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), ensuring transparency throughout the identification, screening, eligibility, and inclusion stages (Page et al., 2021). Searches were conducted in ERIC and Google Scholar using terms related to AI, UDL, mathematics, problem-solving, engagement, and inclusion. Following the identification, screening, and eligibility stages, 16 studies were included in the qualitative synthesis. The findings suggest that AI primarily supports personalization, scaffolding, feedback, and problem-solving, whereas UDL helps reduce learning barriers by providing multiple means of representation, engagement, action, and expression. Student engagement emerged as an important point of connection between the two approaches. However, relatively little empirical evidence was found on school-based interventions that explicitly integrate AI and UDL in mathematics education. Overall, the findings suggest that AI and UDL offer potentially complementary pedagogical mechanisms, although further research is needed to examine their deliberate integration and its effects on mathematics learning, problem-solving, and inclusive participation.
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