Modeling First: Rethinking Undergraduate Operations Management with AI

As part of an institutional initiative to differentiate core undergraduate business classes, we have recently launched a new operations management (OM) course focused on modeling. Whereas the Traditional OM (TOM) course reviews classical models in inventory, queueing, and supply chains, the new Operations Modeling with AI (OMAI) course centers on the practice of mathematical modeling, particularly optimization and simulation. A distinctive element of OMAI is the integration of generative AI–based pair-programming assistants through what we call Incremental Prompting, which prioritizes learning essential modeling skills—abstracting real situations and expressing them with mathematical precision—rather than programming syntax. We evaluated the course through an end-of-term self-assessment survey across both offerings (n = 250; TOM = 156, OMAI = 94). Results indicate that OMAI students reported greater comfort and confidence in understanding, valuing, and applying model-based decision making. Interestingly, these findings become more pronounced after controlling for prior coding experience, suggesting that OMAI benefits both technically experienced and less experienced learners. In survey responses, students credited the pair-programming framework with their increased comfort and understanding of modeling. Taken together, our findings demonstrate how generative AI can be leveraged in OM and OR education to reduce technical barriers and sharpen focus on core modeling skills. Funding: A. Daw was supported by the National Science Foundation Division of Civil, Mechanical, and Manufacturing Innovation [Grant CMMI-2441387].