Optimization of Mathematics Learning through Adaptive Algorithms in Intelligent Educational Platforms
DOI:
https://doi.org/10.63969/e3cym883Keywords:
adaptive learning, artificial intelligence, mathematics, intelligent educational platforms, educational personalization, adaptive algorithmsAbstract
The advancement of artificial intelligence has promoted significant transformations in contemporary educational processes, particularly in mathematics teaching through intelligent educational platforms based on adaptive algorithms. This article aims to analyze, from a narrative literature review, the contributions, limitations, and challenges of adaptive algorithms in the optimization of mathematics learning. The study was conducted under a qualitative, documentary, and descriptive approach through the analysis of scientific literature published between 2019 and 2026 in academic databases such as Scopus, Web of Science, ERIC, SciELO, and Google Scholar. Studies related to educational artificial intelligence, adaptive learning, educational analytics, and intelligent platforms applied to mathematics education were reviewed. The analyzed literature shows that adaptive algorithms promote learning personalization, strengthen academic performance, enhance student motivation, and contribute to reducing mathematics anxiety. Likewise, intelligent platforms make it possible to identify cognitive difficulties through immediate feedback systems and individualized learning pathways. However, limitations associated with the digital divide, insufficient teacher training, limited technological infrastructure, and ethical concerns regarding student data management are also recognized. It is concluded that adaptive algorithms represent a strategic innovation for strengthening contemporary mathematics education, provided that their implementation integrates pedagogy, technology, teacher training, and ethical principles aimed at the responsible use of artificial intelligence.
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Copyright (c) 2026 José Aníbal Angulo De León , Blanca Romina Barcia Rivera , Erika Gissella Vera Benitez , María Gabriela Zambrano Álvarez (Autor/a)
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