## 2 Materials and Methods  
The PRISMA 2020 Statement has been used for this review [21]. strictly complying with the PRISMA 2020 Checklist (see Supplementary Materials). This allowed us to develop a systematic literature review as a strategy to identify the most relevant studies on generative artificial intelligence in higher education, considering that these reviews are used to identify, interpret and evaluate the data available in scientific production in a given period [22]. Operationally, the study followed a systematic review process consisting of five phases [23], specifying the PRISMA 2020 methodological criteria--eligibility criteria, sources of information, search strategy, study selection process, data extraction process, list of extracted data, assessment of risk of bias of individual studies, synthesis methods, assessment of publication bias, and assessment of certainty of evidence [21]. It is worth mentioning that the effect measurement criterion does not apply in this study. Phase 1: Research questions (RQ). They are organized around three areas: (a) Conceptual Framework, to analyze the relationships between keywords identified in the literature (RQ1); (b) Bibliometric Performance, to identify Types of Documents, Geographic Location, Thematic Areas, and Words' Frequency over Time (RQ2-RQ5); and (c) Transversal competencies (RQ6), to categorize the transversal competencies associated with generative artificial intelligence studies in higher education (see Table 1). Phase 2: Eligibility criteria and sources of information. We included articles published in Web of Science (WoS), Scopus, and PubMed between 2023 and 2025 that contain in their title the descriptors used in the search equation presented below, in English or Spanish. It is revealing that, despite not having executed temporal restrictions in the search, all the studies found date from 2023 onwards. The eligibility criteria were mainly based on the elements of the study's PICO Question: What are the transversal competencies associated with studies on the use of generative artificial intelligence tools in higher education, considering professors, students, and institutions? * Population: Teachers, students, or institutions of higher education. * Intervention: Use of generative artificial intelligence tools. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Systematization Criteria** & **Research Questions** & **Procedure** \\ \hline \multirow{3}{*}{Transversal Competencies} & RQ1: What are the Transversal Competencies & Identification of competencies \\  & associated with generative artificial intelligence studies in higher education? & through theoretical systematization \\ \hline \multirow{3}{*}{Conceptual Framework} & RQ2: What is the conceptual structure of research on generative artificial intelligence in higher education and strategic processes that promote its sustainable development in this field? & Analysis of co-occurrences of key terms and concepts in VOSviewer and Coding in Atlas.ti \\ \hline \multirow{3}{*}{Bibliometric Performance} & RQ3: What is the distribution of scientific production by type of document? & Classification by document type \\  & RQ4: How does the geographical distribution of publications behave? & Identification of the country of affiliation of the first author and visualization through maps (Bibliometric) \\ \cline{1-1}  & RQ5: What thematic areas stand out in the scientific production on the use of generative AI in higher education? & Grouping by academic disciplines \\ \cline{1-1}  & RQ6: What are the most frequent terms associated with generative AI in higher education, and how has their frequency of occurrence in the scientific literature varied between 2023 and 2025? & Words’ Frequency over Time (Bibliometric) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Research questions. Results: Most relevant transversal competencies associated with the study and use of Generative AI in higher education.