Best Practices for Large Language Models (LLMs) in Chemical Engineering Education

The Chemical Engineering (ChemE) Department prides itself in preparing the next generation of engineers and is dedicated to being a leader in engineering education. However, our industry, like others, is already quickly changing due to capable and accessible artificial intelligence (AI) technology and large-language models (LLMs). Following a preliminary study and discussion during our IAP 2026 faculty retreat, our department emerged committed to determining how to adapt and deploy new pedagogy across our undergraduate curriculum. This proposal outlines a plan to achieve these goals. It aims to build and execute an evidence-based implementation plan that leverages a departmental action team (DAT) to strengthen, rather than replace, the mastery in subject matter our students need to continue to succeed as future leaders in a rapidly transforming field. The DAT structure is uniquely suited to refining the broad questions regarding the impact and response of LLMs in ChemE education. A diverse representation of students, instructional staff, and faculty will contribute to an effective, staged effort that informs current and sustained pedagogical practice in the department. Additionally, coordination with institute efforts to examine LLM practices and policies is critical to the DAT and can lay the foundation campus-wide changes in undergraduate education.