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

• María de Los Ángeles Martínez-Mercado ^[1] ; Gisela Elízabeth López-Bustamante ^[2] ; Azucena Minerva García-León ^[1] ; Elva Patricia Puente-Aguilar ^[1] ; Daniela del Carmen Bacre-Guzmán ^[1]
  1. [1] Universidad Autónoma de Nuevo León

     Universidad Autónoma de Nuevo León

     México

     
  2. [2] Pontificia Universidad Católica del Perú

     Pontificia Universidad Católica del Perú

     Perú

     
• Localización: Revista Iberoamericana de Tecnologías del Aprendizaje: IEEE-RITA, ISSN 1932-8540, Vol. 20, Vol. 1, 2025, págs. 338-346
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • 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