A Competency-Based Educational Methodology for Smart Maintenance: Integrating Condition Monitoring, IoT, and Generative Artificial Intelligence

The digital transformation associated with Industry 4.0 has increased the demand for engineers with competencies in condition monitoring, the Internet of Things (IoT), and artificial intelligence (AI). However, evidence on educational methodologies integrating these technologies within a competency-based assessment framework remains limited. This study developed and preliminarily evaluated a competency-based educational methodology and characterized the technical, digital, and analytical competency profiles of Mechanical Engineering students following participation in smart maintenance learning activities. A descriptive–exploratory case study was conducted with 31 students enrolled in an Industrial Maintenance course. The methodology integrated vibration analysis, infrared thermography, acoustic monitoring, IoT-based remote monitoring, and ChatGPT-assisted fault diagnosis. Competencies were assessed using an analytic rubric applied by six faculty evaluators, while students’ perceptions were examined through a 22-item questionnaire. The Global Competency Index for Smart Maintenance (GCIIM) was 72.86%, indicating a moderate overall competency level. Critical evaluation of artificial intelligence achieved the highest score, whereas fault diagnosis, technical communication, and technological adaptability showed the lowest performance. Students reported a favorable overall perception score (75.04%) across the questionnaire dimensions. These findings provide preliminary evidence of the feasibility of an integrated competency-based framework for smart maintenance education and its potential to support the responsible incorporation of digital technologies and artificial intelligence into engineering curricula.