## 3 Materials and Methods  
The study focuses on an analysis of how AI improves data mining, ML, and teaching. The aim is to provide a clear overview of the trends that have marked the development of this topic from the publication of the first article (2006) to the present (2024). To do so, a methodology is used that combines a qualitative systematic review with a detailed bibliometric analysis, allowing the identification of the most researched topics and the directions that future research on this topic could take. The selection of articles was conducted through a search for key terms in Scopus, which allowed obtaining a relevant set of academic publications. In this first stage, a qualitative search technique was applied to select the most relevant articles related to the terms \"artificial intelligence\", \"education\", and \"data mining\". After the initial collection of articles, a sample purification process was carried out to eliminate possible duplicates or inconsistencies in the data, given that variations in the names of the authors or in the abbreviations could alter the results. To do this, the Science Mapping Analysis Tool (SciMAT v1.1.06) was used, which allowed for an effective bibliometric review, visualizing the most recurrent thematic areas and the connections between key terms over time. Through the consultation of 370 publications in Scopus, the most frequent keywords were identified, along with the thematic areas and the connections between them, but only 78 were cited in the article because of their direct relevance. This analysis was extended to the identification of the networks of co-occurrences of terms, using the VOSviewer tool (version 1.6.20, Leiden University, Leiden, The Netherlands). This tool facilitates the creation of network maps, which allow the visualization of the frequency with which certain terms appear and the connections between them through different articles, in addition to representing the intensity of the relationship between the concepts [34]. The relationship between the terms in the map is interpreted based on the strength of the link, which indicates how many articles present both terms together [35]. This co-occurrence analysis allows the identification of trends and patterns in publications related to the improvement that AI represents in data mining, ML, and teaching. Key terms and their connection within articles reveal dominant and emerging research lines in the area. By looking at networks of terms and their relevance scores, it is possible to identify the core topics that have been the subject of research, and those that have the greatest potential for future research [36]. A key part of this analysis is the creation of network maps, which group related terms and show the evolution of topics over time [37]. This visual representation is useful for observing the dynamics of research areas and how they have been changing since 2006. The clusters formed on the map indicate how different research areas are interconnected with each other, although not all areas within a cluster are necessarily directly related to the core research topic [38]. The study employs relevance scoring to rank key terms. The relevance of a term is calculated based on its frequency of appearance in the titles and abstracts of the articles analyzed. This score helps to identify the most representative terms in each period, which in turn facilitates the prediction of possible directions in which future research on this topic will be oriented [39]. The methodological approach presented in this study has a number of limitations that should be considered in future research. First, bibliometric analysis focuses primarily on quantitative aspects, which means that it might not fully capture qualitative aspects, such as the experience and pedagogical context in which AI and ML are implemented.