Abstract:

Artificial intelligence (AI) is rapidly transforming industrial sectors and increasing the demand for engineers capable not only of implementing algorithms but also of identifying opportunities for technological innovation and transfer. However, traditional engineering education often focuses mainly on technical implementation while paying less attention to the connection between technological development, industrial needs, and market viability. To address this gap, universities must adopt learning methodologies that integrate technical knowledge with real-world challenges and entrepreneurial thinking. This paper presents a project-based learning experience implemented in the course Applied Artificial Intelligence within the Bachelor’s Degree in Industrial Electronics and Automation Engineering at the University of Valladolid (Spain) during the 2025–2026 academic year. The activity was developed within the teaching innovation project “Innovative AI Ideas Lab in Engineering: from the classroom to the market”, which aims to promote creativity, innovation, and entrepreneurial skills among engineering students by encouraging them to design solutions with potential industrial application.

Students were challenged to identify a real industrial problem in sectors such as manufacturing, logistics, maintenance, or quality control and propose a potentially marketable AI-based solution. Each team developed a complete proposal including the definition of the industrial problem, the selection of the appropriate AI approach (e.g., machine learning, computer vision, or predictive systems), the system architecture, hardware and software requirements, data flow, model training strategy, and integration within an industrial environment. In addition to the technical design, the activity incorporated elements rarely included in traditional engineering assignments, such as market analysis, identification of potential customers, competitor evaluation, and definition of a viable business model. The final deliverables consisted of a structured technical report and a oral presentation defending the proposal.

Eleven students participated in the activity, generating four project proposals addressing different industrial challenges: a predictive battery replacement system for autonomous drones, an AI-based positioning system for automated guided vehicles (AGVs), a monitoring system for safe human–robot interaction in collaborative robotics, and a generative AI system for producing synthetic industrial datasets.

The educational impact of the activity was evaluated through a questionnaire completed by all participants. The results show a strong positive perception of the methodology. Approximately 91% of students reported improved understanding of how AI algorithms are integrated into real engineering systems, while a similar percentage indicated that the focus on real industrial applications increased their motivation. Around 90% considered that integrating market analysis into a technical subject is valuable for understanding technological decision-making in companies. Students also highlighted that the activity helped them develop communication, project structuring, and technical synthesis skills relevant for their final-year projects.

Overall, the experience suggests that combining project-based learning with innovation and market-oriented perspectives can significantly enhance student engagement and professional readiness in engineering education.

Keywords:

Artificial Intelligence Education, Project-Based Learning, Engineering Education, Educational Innovation, Industrial AI Applications.