## 2 Materials and Methods
The process ensures a rigorous and precise selection of relevant literature, providing a solid foundation for the analysis and interpretation of the collected data. Coming back to the five stages of our analysis (Figure 1), the last three stages utilized an integrated development environment for R, specifically RStudio and VOSviewer. R is a programming language specifically for graphics and various statistical computations. RStudio Bibliometrix, developed in the R language, is an open-source environment that provides rich functionalities for the visualization of scientific literature and quantitative analysis. An important feature of Bibliometrix is cluster analysis, which helps researchers to identify and group key topics, journals, or authors in the literature, thus revealing the structure and patterns of the research domain [69; 70]. VOSviewer is a specialized software tool designed for the analysis and visualization of networks within bibliometric data [71]. As demonstrated in the study by [PERSON] et al. [39], VOSviewer is used for analyzing and visualizing complex networks in bibliometric data. In the section related to mixed analysis, we discuss the interconnections between various analyzed elements, such as keywords from our bibliometric dataset. VOSviewer is extensively utilized by the academic \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Exploration Steps** & **Questions on Web of Science** & **Description** & **Query** & **Query** & **Count** \\ \hline  & & & & & \\  & & & & & \\
4 & Title/abstract/keywords & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline
5 & Language & Limit to English & (#10) AND LA = (English) & \#11 & 363 \\ \hline
6 & Document Type & Limit to article & (#11) AND DT = (Article) & \#12 & 287 \\ \hline
7 & Year published & Exclude 2024 & (#12) NOT PY = (2024) & \#13 & 237 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._community [72] to analyze and visually depict networks of scientific information, aiding in the identification of trends, relationships, and patterns within the literature. For data analysis, visualization, and interpretation, the focus was on understanding the dataset, starting with the Preliminary Data Analysis, where the number of sources, the annual growth rate, the average number of citations per document, the number of references, the annual evolution of the scientific output, how many Keywords Plus or authors' keywords were identified, the total number of authors, and how many of them preferred to work individually or to collaborate. In Sources Analysis, the most relevant journals were identified by searching the number of papers, which included the application of AI to regional or local studies published in the dataset. In the Authors Analysis, we followed the scientific evolution of each author over the analyzed period of time, observing the volume of published works, the countries with a higher output, as well as their collaboration mode. The literature review includes extensive information about the works cited worldwide, including the number of citations, authors, regions, journals of publication, datasets used, and abstracts of the works. This analysis helps us to understand the purposes and techniques used by each author. Furthermore, the most frequently used keywords were identified, as well as the bigrams or trigrams at the title or abstract level to be able to observe the similarity between papers. The mixed analysis is composed of two three-field diagrams between countries-authors-journals and affiliations-authors-keywords to illustrate the links between these elements, a map in which the relevant keywords are divided into clusters at the title and abstract levels, a keyword co-occurrence map, and two thematic maps at the Keywords Plus and authors' keyword levels to observe the interconnection between the use of artificial intelligence and local or regional studies.