Title:
Artificial Intelligence Learning: Perceptions and Challenges in the Profile of Industrial Engineering Students [Download]Authors:
María de Los Ángeles Martínez-Mercado, Gisela Elízabeth López-Bustamante, Azucena Minerva García-León, Elva Patricia Puente-Aguilar, Daniela del Carmen Bacre-Guzmán
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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.
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How to cite:
María de Los Ángeles Martínez-Mercado, Gisela Elízabeth López-Bustamante, Azucena Minerva García-León, Elva Patricia Puente-Aguilar, Daniela del Carmen Bacre-Guzmán, "Artificial Intelligence Learning: Perceptions and Challenges in the Profile of Industrial Engineering Students", IEEE-RITA, vol. 20, no. 1, pp. 338-346, Jan. 2025. doi: 10.1109/RITA.2025.3620839