Resumen de Artificial Intelligence Learning: Perceptions and Challenges in the Profile of Industrial Engineering Students

María de Los Ángeles Martínez Mercado, Gisela Elízabeth López Bustamante, Azucena Minerva García León, Elva Patricia Puente Aguilar, Daniela del Carmen Bacre Guzmán

• This study analyzes the perception and level of learning in artificial intelligence (AI) topics among Industrial Engineering students at a university in northern Mexico. Using a quantitative approach, a survey was administered to 64 students, focusing on dimensions such as perceived learning, academic and professional use of AI, and the perceived importance of its curricular integration. The findings reveal a limited perception of AI learning among Industrial Engineering students, with the Internet of Things and Data Security and Protection emerging as the highest-rated topics. In contrast, low levels of learning were reported in Predictive Maintenance, Deep Learning, and Quality Control. While 85% of participants consider the inclusion of AI in the curriculum to be essential, only 50% report using these tools in workplace settings. A strong association was identified between Predictive Maintenance and Quality Control, suggesting thematically relevant links for the discipline. These results highlight a gap between theoretical training and practical application of AI, indicating clear opportunities to strengthen its curricular integration