Artificial Intelligence Adoption in Latin American Mathematics Education: Challenges and Opportunities

Abstract

This doctoral research explores the conditions, barriers, and pedagogical implications of adopting Artificial Intelligence (AI) in mathematics education across Latin America. Grounded in an integrative framework that combines the Ontosemiotic Approach (OSA), the Didactic-Mathematical Knowledge and Competences (DMKC) model, the extended TPACK-XK framework, and the Theory of Instrumental Genesis (TIG), the study employs a mixed-method design involving 480 mathematics educators from Panama, Mexico, Argentina, Chile, Colombia, and Brazil. The research investigates regional and gender disparities, teacher training challenges, and institutional readiness for AI-enhanced instruction. Results show significant gaps in digital infrastructure, particularly in rural areas, and a marked gender divide in perceptions of students’ digital skills and AI competence. The study also evaluates design-based training cycles aimed at fostering reflective and context-sensitive use of AI tools in teaching practice. Findings highlight the urgent need for equity-driven AI training policies, professional development programs, and inclusive strategies that strengthen teachers’ technological and didactic capacities. The proposed analytical framework enables a nuanced assessment of AI integration in mathematics education and supports a more adaptive, ethical, and context-aware digital transformation in the region.