{"title":"Exploring the use of large language models (LLMs) in chemical engineering education: Building core course problem models with Chat-GPT","authors":"Meng-Lin Tsai, Chong Wei Ong, Cheng-Liang Chen","doi":"10.1016/j.ece.2023.05.001","DOIUrl":null,"url":null,"abstract":"<div><p>This study highlights the potential benefits of integrating Large Language Models<span><span> (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 </span>Industry<span> 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.</span></span></p></div>","PeriodicalId":48509,"journal":{"name":"Education for Chemical Engineers","volume":null,"pages":null},"PeriodicalIF":3.5000,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Education for Chemical Engineers","FirstCategoryId":"95","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1749772823000180","RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION, SCIENTIFIC DISCIPLINES","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
Education for Chemical Engineers was launched in 2006 with a remit to publisheducation research papers, resource reviews and teaching and learning notes. ECE is targeted at chemical engineering academics and educators, discussing the ongoingchanges and development in chemical engineering education. This international title publishes papers from around the world, creating a global network of chemical engineering academics. Papers demonstrating how educational research results can be applied to chemical engineering education are particularly welcome, as are the accounts of research work that brings new perspectives to established principles, highlighting unsolved problems or indicating direction for future research relevant to chemical engineering education. Core topic areas: -Assessment- Accreditation- Curriculum development and transformation- Design- Diversity- Distance education-- E-learning Entrepreneurship programs- Industry-academic linkages- Benchmarking- Lifelong learning- Multidisciplinary programs- Outreach from kindergarten to high school programs- Student recruitment and retention and transition programs- New technology- Problem-based learning- Social responsibility and professionalism- Teamwork- Web-based learning