Teaching Applied Optimization with Large Language Models

Teachers face both push and pull to address large language models. The pull comes from the increasing market demand for critically assessing or using generative AI tools, while the pressure arises from students’ independent adoption of generative AI, regardless of faculty approval. A case study at Carnegie Mellon University during Fall 2023 with 66 diverse students demonstrated significant shifts when large language models were used. Notably, office hours for technical Python support dropped significantly, and the learning environment became more equitable, allowing students of varying technical backgrounds to progress at similar rates. Additionally, students showed slight improvements in problem framing but no increase in the time spent analyzing results.

Surveys conducted post-course revealed uniform feedback. Students effectively used ChatGPT for coding tasks like debugging and learning Python syntax. ChatGPT, combined with OptiGuide, showed useful but unpredictable results in modifying existing models. The survey also highlighted time savings in assignments and projects, especially where clear instructions mimicked the prompts needed for ChatGPT. Interestingly, two distinct student profiles emerged: learners who used ChatGPT to enhance understanding and those who sought to expedite course completion, utilizing any time saved for other activities.