Transformación Docente con IA: Agenda Institucional para Universidades de México y la Región

This article presented a critical and synthetic review of recent literature (2014–2025) on university teaching excellence and the pedagogical use of artificial intelligence (AI) across four domains didactic planning, assessment of learning, personalization, and content development with the aim of deriving actionable guidance for Mexico and Latin America. A narrative review was conducted with purposive searches in indexed databases and regional repositories, using inclusion criteria based on relevance, methodological rigor, and salience to higher education; empirical studies, reviews, and policy documents were examined. The analysis integrated, on the teaching-excellence strand, learning-centered approaches, formative assessment, digital competencies, and professional ethics, and, on the AI strand, evidence that indicated time savings in planning, richer feedback, adaptive scaffolding, and assisted generation of materials; at the same time, risks related to privacy, bias, algorithmic opacity, and dehumanization were identified, which required regulatory frameworks, transparency, and AI literacy. Based on the synthesis, strategies were proposed: rubrics and peer assessment guides, course embedded prompt templates, human verification protocols, criteria for authentic assessment, and tools for reflective practice. It was concluded that teaching excellence and the responsible integration of AI operated as interdependent processes that required pedagogical, digital, and ethical capacities, as well as institutional support; professional development programs and data governance were recommended, and future lines of inquiry were outlined regarding impacts on learning, equity, and academic working conditions.