Artificial Intelligence applied to teaching-learning process in mathematics education: a bibliometric analysis
DOI:
https://doi.org/10.61347/rien.v2i2.80Keywords:
Artificial intelligence, bibliometrics, bibliometrix, education, machine learning, mathematicsAbstract
The integration of artificial intelligence in mathematics education has gained increasing prominence in recent years, promoting research aimed at improving teaching and learning processes through advanced algorithms and adaptive digital environments. In this context, the present study aims to analyze the evolution, trends and collaboration networks around the scientific production on AI in mathematics education through a bibliometric approach. The methodology was based on data extraction from the Scopus database, considering publications between 2020 and 2025 and including articles, reviews, conferences and book chapters. The data were processed using the Bibliometrix tool through the Biblioshiny interface. The results show an exponential growth in scientific production, with a notable increase from 2022 and a peak in 2024. In terms of leadership, China, the United States and India concentrate the largest production, accompanied by institutions such as the University of Florida and King Abdulaziz University, while authors such as Wang Y. and Li C. are among the most productive. The analysis of frequent terms shows the centrality of machine learning, deep learning and the recent trend of generative AI. Finally, the collaboration networks reflect regional blocs led by scientific powers, with less participation from developing countries. This study provides an updated and systematic view of the relationship between AI and mathematics education, contributing to the understanding of its global dynamics.
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