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

IF 3.5 2区 教育学 Q1 EDUCATION, SCIENTIFIC DISCIPLINES
Meng-Lin Tsai, Chong Wei Ong, Cheng-Liang Chen
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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.

探索在化学工程教育中使用大型语言模型(llm):用Chat-GPT构建核心课程问题模型
这项研究强调了将大型语言模型(LLM)整合到化学工程教育中的潜在好处。在这项研究中,Chat GPT,一种用户友好的LLM,被用作解决问题的工具。化学工程教育传统上侧重于课堂上的基础知识,而动手解决问题的机会有限。为了解决这个问题,我们的研究提出了LLM辅助解决问题的程序。这种方法可以促进批判性思维,提高解决问题的能力,并有助于加深对核心主题的理解。此外,将编程融入化学工程教育,为学生培养现代工业实践的重要工业4.0技能。在我们的实验讲座中,我们介绍了一个建立模型来计算汽轮机循环效率的简单例子,并为学生分配了一些项目,以探索LLM在解决化学工程各个方面问题中的可能用途。尽管学生们对它的反馈褒贬不一,但人们发现它是一种易于使用和实用的工具,可以提高解决问题的效率。通过对学生项目的分析,我们发现了五个常见的困难和误解,并为克服这些困难和误解提供了有益的建议。我们的课程在使用高级工具和解决复杂问题方面存在局限性。我们进一步提供了两个额外的例子,以更好地展示如何将LLM集成到核心课程中。我们强调大学、教授和学生积极接受和利用LLM作为化学工程教育工具的重要性。学生必须培养批判性思维技能,全面理解LLM背后的原则,并对其使用和创造负责。这项研究为通过整合LLM来提高化学工程教育的学习体验和成果提供了宝贵的见解。
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来源期刊
CiteScore
8.80
自引率
17.90%
发文量
30
审稿时长
31 days
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
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