Abstract:

The rapid development of Generative Artificial Intelligence (GenAI) is creating new opportunities for innovation in higher education. Universities should promote initiatives that incorporate GenAI into teaching and learning processes while ensuring appropriate faculty training and responsible student use. In this context, from the perspective of Industrial Engineering education, students must learn how these tools can (or cannot) support the solution of practical engineering problems and, at least during the initial stages, this learning process should take place under faculty supervision. This approach not only modernises educational practice but also prepares graduates with the critical and technical competences required in an increasingly digitalised professional environment.

This paper presents the implementation of four GenAI-based activities carried out during the first semester of the 2025–2026 academic year at the School of Industrial Engineering, University of Valladolid (Spain), within the framework of an Innovative Educational Project. The activities, in which students used GenAI tools to address applied engineering tasks, were integrated into the following courses: “Control Systems Design”, to select electronic circuits and commercial components to physically implement a previously designed PID controller; “Industrial Informatics”, to generate C++ class libraries for industrial sensing; “Environmental and Industrial Noise Management”, to solve acoustic-based case studies including spectral representation checks; and “Occupational Hygiene”, to analyse occupational risks and calculate dilution ventilation airflow rates for chemical hazards. Across these activities, students learned to critically evaluate the coherence and technical validity of the AI-generated responses, identifying recurring issues such as unreliable references, inconsistencies in reasoning, numerical errors, and confusion in specialised terminology. These observations highlighted the importance of comparing GenAI outputs with solid prior knowledge.

Anonymous surveys conducted at the end of the activities revealed highly positive perceptions: 89% of students found the activities engaging and motivating, and the development of critical thinking received an average score of 4.4 out of 5. Additionally, lecturers emphasised the need to strengthen ethical reflection and to train students in effective prompting strategies for future activities. Collectively, these experiences suggest that GenAI can constitute a powerful and motivating educational resource when used within a supervised framework. However, its effective integration in engineering education requires students to possess strong technical foundations and to develop the ability to critically evaluate automated results. The experiences described demonstrate that combining GenAI tools with guided reflection enhances learning outcomes and contributes to preparing future engineers to responsibly address the challenges posed by emerging AI-based technologies.

Keywords:

Generative Artificial Intelligence, Industrial Engineering Degrees, Critical Thinking, Innovative Educational Project.