## 3 Results
This implies developing clear policies on data privacy, copyright, and algorithmic biases, as well as promoting critical literacy for students and faculty to understand how generative models are trained, operate, and mutate. Only through an institutional culture that recognizes the ambivalence of Generative Artificial Intelligence--its innovative potential and its threats to academic integrity--will it be possible to ensure its responsible and sustained use over time. At the same time, institutional management of technological infrastructure requires aligning intelligent and contextualized investment in technological infrastructure--servers, cloud platforms, software licenses--with governance strategies that respond to the mission and characteristics of each university. This implies designing cross-cutting committees involving ICT, student welfare, research, and teaching areas, so that adoption decisions, monitoring of use, and updating of models are made in a contextualized, sustainable manner and with full knowledge of the financial, ethical, and social impacts. Teacher development is vital to sustain Generative Artificial Intelligence in academia; teachers must have continuous training that combines technical aspects (model training and tuning, data governance) with pedagogical reflection on its applications and limitations. Mentoring programs, collaborative workshops, and communities of practice allow teachers to share experiences, co-create teaching resources, and keep abreast of a rapidly evolving field, ensuring that the integration of Generative Artificial Intelligence is coherent, innovative, and aligned with institutional goals. Teacher development allows a curricular transformation that goes beyond the mere adoption of tools; it means rethinking learning objectives, evaluation methodologies, and content structure to take advantage of generative capabilities. Instead of treating Generative Artificial Intelligence as a complement, curricula should incorporate activities where language modeling and content generation become pedagogical axes, fostering human-machine co-creation competencies, critical thinking about automatically generated results, and adaptability to emerging information environments. All this is the basis for driving pedagogical innovation powered by Generative Artificial Intelligence, which opens the door to unprecedented instructional designs: high-fidelity simulated laboratories, personalized tutorials with generative chatbots, and adaptive learning environments that respond in real time to the performance of each student. These methodologies promote an active role for the learner, facilitate safe experimentation, and allow for continuous formative assessments based on automated analysis of creative and cognitive processes, thus enriching the teaching-learning process. RQ3. What is the distribution of the scientific production of generative artificial intelligence in higher education according to the type of document? The distribution of scientific production on generative artificial intelligence in higher education shows a clear predominance of academic articles, followed by papers presentedat conferences. This suggests that research in this field is mainly disseminated through peer-reviewed publications and specialized congresses, reflecting the relevance of the topic in the academic community. To a lesser extent, book chapters and reviews are found, indicating that there is also an effort to consolidate knowledge in more structured formats. Other types of documents, such as editorials, notes, books, letters, and errata, have little presence (see Figure 5). As can be seen, the number of reviews, especially systematic ones, is considerably lower, which points to a crucial opportunity and need to strengthen this type of study to synthesize existing knowledge, identify trends, assess the impact of Artificial Intelligence in education, and guide future research with a solid foundation.