ARTIFICIAL INTELLIGENCE IN ELECTROMECANICAL ENGINEERING: THE ESPRIT MODEL

In response to the surge of artificial intelligence (AI) in the last decade, which now spans across electromechanical sectors like automation, electricity, and maintenance, the ESPRIT approach is introduced. It emphasizes the need for engineers to diversify their skill sets to adapt to the evolving landscape. This educational paradigm integrates an AI module into the electromechanical engineering curriculum, congruent with CDIO standards, to cultivate a broad spectrum of competencies in AI. The curriculum is meticulously crafted to progress from foundational knowledge to advanced application and assessment, employing active learning strategies to enhance students’ technical, problem-solving, and professional skills, ultimately encouraging a well-rounded mastery of AI in engineering. This paper describes the ESPRIT approach, a pedagogical paradigm tailored for equipping electromechanical engineers with the necessary AI competencies. The integration of a dedicated AI module within ESPRIT’s electromechanical engineering curriculum aligns with the CDIO standards, marking a significant stride in engineering education. Our pedagogical contribution is threefold, encapsulating the design, execution, and evaluation of the AI module over a span of three years. The curriculum employs active learning strategies (standard 8) to immerse students in AI problem-solving, fostering an environment of practical engagement. The curriculum unfolds in a structured manner (standard 3), starting with the AI discovery phase in the third year, where students acquaint themselves with Python, AI libraries, and foundational AI concepts, including elementary classification and regression algorithms. The second phase, in the fourth year, pivots on the application and reinforcement of the knowledge acquired, with a focus on the lifecycle of an AI project. Students culminate this stage by undertaking a mini project adhering to AI project conventions. The final phase, in the fifth year, emphasizes practical application and mastery, culminating in an NVIDIA DLI workshop where students have the opportunity to earn a certificate in AI for predictive maintenance. In conclusion, the paper presents a critical analysis of this pedagogical approach, highlighting its pragmatic applications and the well-paced learning trajectory that aligns with student capability. Nonetheless, it underscores the imperative of achieving a symmetrical balance between the theoretical and practical aspects of AI to fully harness its potential in electromechanical engineering.