Artificial Intelligence in Mathematics teaching and learning: a systematic review (2020–2026)
DOI:
https://doi.org/10.51247/st.v9i3.769Keywords:
artificial intelligence; mathematics education; intelligent tutoring systems; adaptive learningAbstract
The aim of this study was to analyze and synthesize the scientific evidence on the application of artificial intelligence (AI) in mathematics teaching and learning during the period 2020–2026, with the purpose of identifying the predominant typologies, their effects on academic performance, and the main associated ethical and pedagogical challenges. The methodology was based on a systematic literature review following the PRISMA 2020 protocol guidelines. The search was conducted in a structured manner in seven high-impact scientific databases, resulting in a final corpus of 32 empirical studies and systematic reviews. The results show that Intelligent Tutoring Systems constitute the most established and frequent application, followed by generative AI and predictive analytics tools. Furthermore, the reviewed literature shows a positive association between the use of AI and improved mathematical learning, although a limited number of studies report standardized effect sizes that would allow for a precise assessment of the magnitude of these benefits. It is concluded that AI has significant potential to strengthen the teaching and learning processes of mathematics at different educational levels; however, challenges related to ethics, algorithmic transparency, and teacher training remain. The originality of this study lies in offering an updated and specific synthesis for mathematics education, contributing to guiding future research and evidence-based educational decisions.
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Copyright (c) 2026 Mario Ibarra-Martinez, Manuel Eduardo Lopez-Delgado
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