Exploring the use of large language models (LLMs) in chemical engineering education: Building core course problem models with Chat-GPT

• Published in: Education for chemical engineers Vol. 44; pp. 71 - 95
• Main Authors: Tsai, Meng-Lin, Ong, Chong Wei, Chen, Cheng-Liang
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2023
• Subjects: Chat-GPT; Engineering education; Industry 4.0 skill; Large language models (LLMs); Problem-solving; Programming in chemical engineering; Industry 4.0 skill; Programming in chemical engineering; Chat-GPT; Problem-solving; Engineering education; Large language models (LLMs)
• ISSN: 1749-7728; 1749-7728
• DOI: 10.1016/j.ece.2023.05.001

This study highlights the potential benefits of integrating Large Language Models (LLMs) into chemical engineering education. In this study, Chat-GPT, a user-friendly LLM, is used as a problem-solving tool. Chemical engineering education has traditionally focused on fundamental knowledge in the classroom with limited opportunities for hands-on problem-solving. To address this issue, our study proposes an LLMs-assisted problem-solving procedure. This approach promotes critical thinking, enhances problem-solving abilities, and facilitates a deeper understanding of core subjects. Furthermore, incorporating programming into chemical engineering education prepares students with vital Industry 4.0 skills for contemporary industrial practices. During our experimental lecture, we introduced a simple example of building a model to calculate steam turbine cycle efficiency, and assigned projects to students for exploring the possible use of LLMs in solving various aspect of chemical engineering problems. Although it received mixed feedback from students, it was found to be an accessible and practical tool for improving problem-solving efficiency. Analyzing the student projects, we identified five common difficulties and misconceptions and provided helpful suggestions for overcoming them. Our course has limitations regarding using advanced tools and addressing complex problems. We further provide two additional examples to better demonstrate how to integrate LLMs into core courses. We emphasize the importance of universities, professors, and students actively embracing and utilizing LLMs as tools for chemical engineering education. Students must develop critical thinking skills and a thorough understanding of the principles behind LLMs, taking responsibility for their use and creations. This study provides valuable insights for enhancing chemical engineering education's learning experience and outcomes by integrating LLMs. [Display omitted] •LLMs-assisted problem-solving procedure cultivate hands-on problem-solving and programming skills in ChemE education.•Student feedback on LLMs-assisted problem-solving procedure has been collected.•Suggestions to overcome misconceptions about integrating LLMs into ChemE education.•Provide examples and suggestions to aid integration of LLMs-assisted problem-solving procedure in ChemE education.•Recommendations for universities, ChemE professors, and students to adopt LLMs-assisted problem-solving procedure.