## 3 Results
Of the 35 studies included, 27 showed a low risk of bias, indicating strong methodological quality, while the 8 with moderate risk had lower citation impact, suggesting a limited effect on the conclusions. No eligible studies were excluded, supporting the comprehensiveness and reliability of the synthesis. RQ2: What is the conceptual structure of research on generative artificial intelligence in higher education and strategic processes that promote sustainable development in this field? VOSviewer version 1.6.18 software was used to create the conceptual network, which allowed not only the identification of the most relevant concepts but also the association between them (see Figure 3). The yellow cluster mainly addresses the ethical and educational competencies needed for the proper use of generative artificial intelligence, with emphasis on digital literacy and academic integrity; the purple cluster represents connections related to ethics and learning processes linked to the adoption and use of technologies such as ChatGPT, the green cluster focuses on the use of educational technologies and educational computing in higher education and how these influence behavioral intentions and student experience, the blue cluster with main concepts such as: \"Large language model\", 'language processing', 'education computing', focuses on the technical aspects of language models, while the red cluster with concepts such as: 'Teaching', 'curricula', 'contrastive learning', 'federated learning' connotes innovative pedagogical methodologies, curriculum design and collaborative learning approaches mediated by generative artificial intelligence. Each cluster reflects a key theme within the conceptual framework of generative artificial intelligence in higher education, showing the interactions between the concepts and their relevance in the development of this field. The analysis of clusters and their connections contributes to creating a holistic conceptual framework on generative artificial intelligence in higher education, integrating several key perspectives and dimensions that interact synergistically. Subsequently, a thematic analysis was performed in ATLAS.TI version 8, which allowed for a categorization process by grouping the codes into new emerging categories that constitute key processes for the sustainability of generative artificial intelligence in the development of transversal competencies in higher education (see Figure 4). Figure 4: Processes for the sustainability of Generative Artificial Intelligence in Higher Education. Figure 3: Cooccurrence conceptual network. Note: Visualization of terms that appear together frequently in publications on artificial intelligence in higher education. Colors represent related thematic clusters, and the size of the nodes indicates the relevance of the term in the dataset. Connections show associations between key concepts. It is revealed that the sustainability of Generative Artificial Intelligence in higher education lies in the synergy between ethical appropriation, contextualized institutional management in technological infrastructure, teacher development, curricular restructuring, and pedagogical innovation. These five processes form an ecosystem where each element reinforces the others: without a solid ethical framework, pedagogical innovations can generate risks; without strategic institutional management, curricular transformation efforts lack support; and without trained teachers, generative tools will never reach their full educational potential. Only by coherently articulating these dimensions will we consolidate a Generative Artificial Intelligence integration model that is at once innovative, responsible, and enduring. The critical and ethical appropriation of Generative AI requires higher education institutions to establish regulatory frameworks and spaces for reflection where both the capabilities and risks of these technologies are evaluated.