INTELLIGENT TUTORING SYSTEMS (ITS) BASED ON ARTIFICIAL INTELLIGENCE (AI) APPLIED TO THE TEACHING AND LEARNING OF MATHEMATICS
DOI:
https://doi.org/10.56219/trascendere.v2i7.5624Keywords:
Artificial intelligence, educational technology, intelligent tutoring systems, mathematics, systematic reviewAbstract
The purpose of this systematic review was to analyze the theoretical, technological, and pedagogical advances in the development and application of Intelligent Tutoring Systems (ITS) in the teaching and learning of mathematics, in order to identify recent trends, empirical contributions, and methodological challenges during the 2021–2025 period. The study followed the methodological guidelines proposed by Kitchenham and Charters (2007) and the PRISMA (2020) framework. It involved a comprehensive search across high-impact academic databases such as Scopus, Web of Science, ERIC, ACM Digital Library, SciELO, Redalyc, and Dialnet, using Boolean combinations in English and Spanish related to “Intelligent Tutoring Systems” and “Mathematics Education.”
Out of fifteen identified studies, eleven met the inclusion criteria established under the PICOS model and were analyzed through three main axes: technological-pedagogical, cognitive-didactic, and evaluative. The findings reveal that the integration of adaptive models, learning analytics, and personalized feedback improves both diagnostic accuracy and student motivation. Likewise, ITS, when implemented with teacher mediation, enhance conceptual understanding, self-regulation, and positive attitudes toward mathematics.
However, methodological challenges persist, including design heterogeneity, the scarcity of longitudinal studies, and the lack of standardized metrics for assessing deep learning. Overall, the results confirm that ITS represent a highly promising tool for achieving more personalized, reflective, and evidence-based mathematics instruction—provided that their implementation is grounded in sound pedagogical and ethical principles.
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