2025 AIChE Annual Meeting

(262d) Attitudes Toward the Use of AI in Chemical Engineering Senior Design

Authors

Lisa Bullard, North Carolina State University
Matthew Cooper, North Carolina State University
Artificial Intelligence (AI) tools have created a unique opportunity for ChE education as they can reshape modern pedagogical best practices. Specifically, large language models (LLM) such as ChatGPT or Microsoft Copilot have been shown to be highly valuable supplemental education tools. In particular, LLMs can be a powerful tool for many design scenarios and in many cases can provide relatively accurate information on process design, making them potentially valuable for integration into ChE capstone design courses. Specific use cases include informing the framework of design for particular process equipment, troubleshooting error messages in ASPEN simulations, and even solving complex design equations. AI used in this manner could also efficiently supplement a significant portion of instructor and/or mentor time investment. If used correctly, it can provide positive outcomes towards student learning objectives as well as increase instructor effectiveness. However, the growth of LLM capability has been so rapid that many university faculty have struggled to effectively adopt AI into their curricula. This has created a gap in consistent implementation that can be closed with further pedagogical study and understanding of practitioner perspectives on the use of AI in real-world engineering work.

In this study, we surveyed approximately 50 private- and public-sector mentors of senior design projects in the Chemical and Biomolecular Engineering (CBE) department at North Carolina State University (NCSU) to evaluate their personal use of AI and their attitudes toward its application in ChE design tasks. Based on the data collected, mentors of chemical engineering senior design students feel that pervasive AI usage for design work is inappropriate. The attitudes reflect that first- principles understanding, and the core ChE curriculum, are the most important aspects of a chemical engineering degree. However, mentors also acknowledged that AI can help with brainstorming, literature review, and speeding up design work itself, provided students are equipped with the fundamental understanding to critically evaluate the accuracy of answers provided by AI. Additionally, some mentors suggest that learning modules that help students navigate their own understanding supplemented with AI input could be valuable, and suggestions regarding future curriculum development in this regard will be presented as part of this study.