Building Capacity for AI in Mechanical Engineering

Abstract:  

Artificial intelligence is reshaping mechanical engineering practice across design, manufacturing, robotics, energy systems, and predictive maintenance. This shift poses an urgent challenge for mechanical engineering departments: how to build practical AI capability in research and education without weakening the engineering fundamentals that ensure safety, reliability, and physical realism. In this seminar, I present an engineering-first framework for building AI capacity, informed by industry expectations and recent results from an SEC-wide faculty survey. I argue that treating AI primarily as a conversational tool, without strong grounding in mechanics, thermodynamics, materials, and controls, can lead to overconfidence and consequential engineering errors. Instead, AI should be integrated to accelerate engineering iteration and insight, while remaining constrained by physics-based reasoning and supported by rigorous evaluation. The seminar concludes by highlighting cross-institutional collaboration to co-develop curricula, shared evaluation frameworks, and research partnerships that advance trustworthy AI for physical systems.