Impact of Teacher Training in Artificial Intelligence on Higher Education: Perceptions, Challenges, and Opportunities - The UTCAM Case

Abstract

Integrating Artificial Intelligence (AI) into higher education requires faculty to build technical skills and critical judgment about how to use these tools; even so, teacher training in this area remains scarce across Latin America. This study examines the effect of a 40-hour intensive AI training program on the perceptions, readiness, and motivation of 102 faculty members at the Universidad Tecnológica de Campeche, Mexico, split into two cohorts (January and March 2025). The research followed a quantitative, descriptive, cross-sectional design with participant observation. Data were collected through a 28-item ad hoc questionnaire covering five domains: Prior Perception and Knowledge, Perceived Effectiveness, Post-training Readiness and Motivation, Institutional Support, and Risks and Challenges. Internal consistency was assessed using Cronbach's Alpha (α from 0.617 to 0.871). Before the training, 83.33% of participants reported no or only basic experience with AI tools, though 80.39% expressed curiosity. After the program, 94.12% reported a clear improvement in their understanding of AI, 98.04% asked for additional training, and 78.43% described AI adoption as inevitable. A gap persists, however, between the institution's stated support (84.31%) and its actually available resources: insufficient tools (75.49%), limited specialized knowledge (68.63%), and tight budgets (50.98%) account for the main barriers. Intensive training does shift faculty confidence and readiness, but that effect only holds when the institution backs it up with real resources, ongoing support, and policies that go beyond rhetoric and good intentions.