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

Abstract

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.