Generative Artificial Intelligence Policies in Three Mexican Universities and Federal Actions: Challenges and Possible Pathways

DOI: https://doi.org/10.54674/ess.v37i2.1135
Keywords: Higher education, public policy, university policy, digital technology

Abstract

The emergence of generative artificial intelligence (GAI) has transformed the educational landscape, prompting higher education institutions to develop policies for its integration. This study analyzes how three Mexican universities—the National Autonomous University of Mexico (UNAM), the Ibero-American University (UIA), and the Monterrey Institute of Technology (ITESM)—have addressed this challenge, as well as the initial federal actions to guide public policy in higher education. A documentary analysis of comparative cases was conducted on the policy actions and instruments related to the adoption of GAI at these universities, as well as at the Secretariat of Public Education (SEP), which corresponds to the national ministry of education. Governance patterns, the timing of actions, and the policy instruments used were identified from a social systems perspective. The three institutions have implemented strategies that combine teacher training, regulatory guidelines, digital infrastructure, and multi-stakeholder governance mechanisms. The cases demonstrate a shift toward the need to develop flexible and collaborative policy-design models that subvert the traditional sequential nature of the public policy cycle.

Author Biographies

Mario Benavides Lara, National Autonomous University of Mexico, Mexico City, Mexico

Ph.D. candidate in Social Sciences at the Autonomous Metropolitan University (UAM), Xochimilco campus; holds a master’s degree and a bachelor’s degree in Education from the National Autonomous University of Mexico (UNAM). He is the deputy director of Knowledge Mobilization in Education at the Coordination of Educational Evaluation, Innovation, and Development (CEIDE) at UNAM. In addition, he is a faculty member in the SUAYED system at the School of Philosophy and Letters. His areas of research include educational and curriculum policies, teacher training, education and gender, technologies, education, and the social studies of education. He is the author of articles, textbooks, and teaching materials.

Víctor Jesús Rendón Cazales, National Autonomous University of Mexico, Mexico City, Mexico

He holds a Ph.D. and a Master’s degree in Science with a specialization in Educational Research from the Department of Educational Research at CINVESTAV-IPN. He serves as the Coordinator of Knowledge Generation and Dissemination in Education within the Coordination of Educational Evaluation, Innovation, and Development (CEIDE) at UNAM. He also teaches in the master’s and doctoral programs in Medical, Dental, and Health Sciences at UNAM, in addition to teaching in the SUAYED system at the School of Philosophy and Letters. He has published works on teacher training, academic writing practices, and the use of digital technologies.

Luz Gisela Macías Carrillo, National Autonomous University of Mexico, Mexico City, Mexico

She holds a bachelor’s degree in Education from UNAM, a master’s degree in Educational Research and Development, and is currently pursuing a Ph.D. in Education, all from the Universidad Iberoamericana. At the Coordination of Educational Evaluation, Innovation, and Development (CEIDE) at UNAM, she collaborates on the development of studies, educational materials, and knowledge mobilization initiatives, in addition to teaching various courses and workshops for faculty. Her main areas of interest include university trajectories, the use of artificial intelligence in education, educational assessment, and the processes of reading, writing, and academic argumentation.

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Published
2026-05-30
How to Cite
Benavides Lara, M., Rendón Cazales, V. J., & Macías Carrillo, L. G. (2026). Generative Artificial Intelligence Policies in Three Mexican Universities and Federal Actions: Challenges and Possible Pathways. Higher Education and Society Journal (ESS), 37(2), 223-248. https://doi.org/10.54674/ess.v37i2.1135
Issue
Vol 37 No 2 (2025): Artificial Generative Intelligence (GenAI) in teaching and research: best practices, challenges, and prospects
Section
Articles Thematic Dossier

Copyright (c) 2025 Mario Benavides Lara, Víctor Jesús Rendón Cazales, Luz Gisela Macías Carrillo

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