Aprendizaje Matemático personalizado mediante inteligencia artificial: un modelo adaptativo para el desarrollo del razonamiento algebraico en educación

Violeta del Carmen Delgado Santin; Milton Luyely Intriago Cedeño; Jennifer Alejandra  Intriago Moreira; Christian Eduardo González Ramírez; Patricia del Pilar Tandayamo Vargas

doi:10.70577/ASCE/311.332/2025

Authors

Violeta del Carmen Delgado Santin Ministerio de Educación del Ecuador https://orcid.org/0009-0002-6679-4942
Milton Luyely Intriago Cedeño Universidad Tecnológica Empresarial de Guayaquil https://orcid.org/0009-0009-2864-133X
Jennifer Alejandra Intriago Moreira Instituto Superior Tecnologico FARO https://orcid.org/0009-0001-5556-6787
Christian Eduardo González Ramírez Independiente https://orcid.org/0009-0009-2601-8229
Patricia del Pilar Tandayamo Vargas Ministerio de Educación del Ecuador https://orcid.org/0009-0006-5939-4819

DOI:

https://doi.org/10.70577/ASCE/311.332/2025

Keywords:

Aprendizaje, Inteligencia Artificial, Razonamiento Algebraico, Modelo Adaptativo, Educación, Matemáticas

Abstract

The goal of this study is to use Artificial Intelligence (AI) to create, deploy, and assess an adaptive model for individualized math learning that will help high school pupils improve their algebraic reasoning skills. The study answers to the urgent needto fix the problems that traditional methods have caused in teaching algebra. These methods often don't take into account how different students learn and how well they understand the material. To do this, an intelligent system was made that uses machine learning algorithms and educational data mining to figure out how smart kids are in real time. As a result, the tools needed for active and self-directed learning are made available in a way that may be changed and adapted. The study used a quasi-experimental design with a pretest-posttest control group and a mixed-methods approach. It also looked at how people interacted with the platform in a qualitative way. There were 240 pupils in the sample, all from three public schools. They were split into two groups: experimental and control. The adaptive approach was used in ordinary math courses for eight weeks. We employed validated tools to measure progress in algebraic reasoning and perception surveys that focused on the individualized learning experience. The experimental group did significantly better at algebra than the control group, with a confidence level of p < 0.01. There was a big improvement in generalization through symbolization and solving equations. The study also showed that students who used the adaptive learning environment had more internal motivation and a better sense of their own math skills. Qualitative results showed that using the system helped improve the model's adaption rules and feedback systems. This study shows that individualized AI-based models could be useful in teaching mathematics, making learning more fair and effective. It suggests that using AI in the classroom not only helps with cognitive variety but also helps students learn algebraic thinking in a more structured and relevant way.

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