{"title":"Application of large language models in engineering education: A case study of system modeling and simulation courses","authors":"Chao Liu, Shengyi Yang","doi":"10.1177/03064190241272728","DOIUrl":null,"url":null,"abstract":"In modern engineering education, the application of digital technologies has significantly improved teaching effectiveness and student learning experiences. This study explores the innovative use of large language models (LLMs) in system modeling and simulation courses. Specifically, LLMs were applied to assist in MATLAB programming tasks, allowing students to learn MATLAB commands and programming techniques more conveniently. Additionally, interactions with LLMs guided students in acquiring cross-disciplinary knowledge related to modeling and simulation. In the context of system modeling and control problems, LLMs were utilized to aid in mathematical logic analysis and reasoning by providing potential solutions. These measures have demonstrated that students’ understanding and mastery of complex concepts were improved, and their interest and initiative in learning were stimulated. This paper summarizes the experiences of integrating LLMs in teaching, discusses their potential advantages and challenges in engineering education, and highlights the importance of incorporating digital technologies, particularly large language models, to support educational innovation.","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":"15 18","pages":""},"PeriodicalIF":16.4000,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/03064190241272728","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
In modern engineering education, the application of digital technologies has significantly improved teaching effectiveness and student learning experiences. This study explores the innovative use of large language models (LLMs) in system modeling and simulation courses. Specifically, LLMs were applied to assist in MATLAB programming tasks, allowing students to learn MATLAB commands and programming techniques more conveniently. Additionally, interactions with LLMs guided students in acquiring cross-disciplinary knowledge related to modeling and simulation. In the context of system modeling and control problems, LLMs were utilized to aid in mathematical logic analysis and reasoning by providing potential solutions. These measures have demonstrated that students’ understanding and mastery of complex concepts were improved, and their interest and initiative in learning were stimulated. This paper summarizes the experiences of integrating LLMs in teaching, discusses their potential advantages and challenges in engineering education, and highlights the importance of incorporating digital technologies, particularly large language models, to support educational innovation.
期刊介绍:
Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance.
Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.