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This study evaluates ChatGPT’s performance in mechanical engineering by using question sets from junior and senior mechanical engineering exams and practice items for the Fundamentals of Engineering Exam (FE) in Mechanical Engineering. Two models are compared: a free version and a paid GPT-4 subscription version. GPT-4 achieved higher accuracy (76% vs 51%), but text-only prompting limitations likely prevent passing the FE exam, and inconsistency and confident errors indicate it suits users with expert knowledge.",{"@graph":69,"@context":127},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/chatgpt-mechanical-engineering-examining-performance-on-the-fe-mechanical-engineering-and-undergraduate-exams/297135/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/chatgpt-mechanical-engineering-examining-performance-on-the-fe-mechanical-engineering-and-undergraduate-exams/297135.png","ImageObject",300,407,{"name":92,"@type":93},"Liam","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-05","2026-09-18",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",13,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119,123],{"name":110,"@type":111,"acceptedAnswer":112},"What is the main goal of this study on ChatGPT in mechanical engineering?","Question",{"text":113,"@type":114},"To examine how ChatGPT performs on mechanical engineering questions and to identify classroom and professional use cases and pitfalls for generative AI in this domain.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How did the researchers test ChatGPT’s performance?",{"text":118,"@type":114},"They presented ChatGPT with question sets from junior and senior mechanical engineering exams from a private university, along with practice questions for the Fundamentals of Engineering Exam (FE) in Mechanical Engineering.",{"name":120,"@type":111,"acceptedAnswer":121},"How did GPT-4 compare with the free ChatGPT version?",{"text":122,"@type":114},"The GPT-4 subscription model significantly outperformed the free version, achieving 76% correct answers versus 51% for the free model.",{"name":124,"@type":111,"acceptedAnswer":125},"Can ChatGPT likely pass the FE exam based on the findings?",{"text":126,"@type":114},"The study concludes that due to text-only input limitations and patterns of inconsistency and confident incorrect answers, neither model is likely to pass the FE exam, and the tool is best suited for users with expert knowledge.","https://schema.org",{"og:url":83,"og:type":129,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":131,"canonical":83},"index,follow",{"doc_id":133,"site_id":62},297135,1789741056,{"code":4,"msg":5,"data":136},{"doc_id":133,"user_id":137,"nickname":92,"user_avatar":138,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":139,"file_id":140,"file_url":141,"file_type":142,"file_size":143,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":144,"language":145,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":146,"faqs":147,"seo_title":148,"seo_description":67,"update_tm":134,"read_time":149,"preview_page_count":4},8796095461564,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","ChatGPT & Mechanical Engineering: Examining performance on the FE Mechanical Engineering and Undergraduate Exams  \nAuthors  \nProf. Matthew E Frenkel  \n[matthew.frenkel@nyu.edu](matthew.frenkel@nyu.edu)  \n(646) 997-3518 Bern Dibner Library  \n5 MetroTech Center, Room 303 Brooklyn, NY 11201  \nORCID: 0000-0002-6883-1105  \nHebah Emara ()  \n[he2156@nyu.edu](he2156@nyu.edu)  \n(646) 997-3527 Bern Dibner Library  \n5 MetroTech Center, Room 302 Brooklyn, NY 11201  \nORCID: 0009-0006-9642-2225  \nAbstract  \nThe launch of ChatGPT at the end of 2022 generated large interest into possible applications of artificial intelligence in STEM education and among STEM professions. As a result many questions surrounding the capabilities of generative AI tools inside and outside of the classroom have been raised and are starting to be explored. This study examines the capabilities of ChatGPT within the discipline of mechanical engineering. It aims to examine use cases and pitfalls of such a technology in the classroom and professional settings. ChatGPT was presented with a set of questions from junior and senior level mechanical engineering exams provided at a large private university, as well as a set of practice questions for the Fundamentals of Engineering Exam (FE) in  \nMechanical Engineering. The responses of two ChatGPT models, one free to use and one paid subscription, were analyzed. The paper found that the subscription model (GPT-4) greatly outperformed the free version (GPT-3 .5), achieving 76% correct vs 51% correct, but the limitation of text only input on both models makes neither likely to pass the FE exam. The results confirm findings in the literature with regards to types of errors and pitfalls made by ChatGPT. It was found that due to its inconsistency and a tendency to confidently produce incorrect answers the tool is best suited for users with expert knowledge.  \nKeywords  \nFundamentals of Engineering Exam; Scientific Literacy; ChatGPT; Engineering Education; Mechanical Engineering  \nIntroduction  \nThe introduction of the electronic calculator revolutionized the jobs of engineers. The tedious and time-consuming task of consulting trigonometric and logarithmic tables was reduced to pressing a few buttons. In much the same way, the calculator also changed the engineering classroom for students and teachers. The electronic calculator is just one of many disruptive technologies introduced in the 20th and 21st centuries that altered the landscape of the engineering workplace and classroom. Computer Aided Design (CAD) software changed the process of engineering drafting. Personal and high performance computing changed the timescales needed for complex calculations and simulation. The modern internet remade the platforms used for communication and collaboration. Once the utility of these disruptive technologies is understood by the  \nengineering workforce, their adoption becomes ubiquitous and as a result they also become an important element of engineering education. University students around the world are entering classrooms with calculators and laptops in hand, collaborating on learning management systems, and engineering software such as MATLAB, Ansys, and SOLIDWORKS are being directly integrated into classroom curriculum.  \nRecent developments in publicly available artificial intelligence technologies, specifically large language models (LLMs) may be the next disruptive technology that will reshape the engineering profession and engineering education. Generative AI, made possible through LLMs, such as ChatGPT have seen tremendous growth in usage recently and the profession has begun to react. Academic journals are implementing policies around ChatGPT authorship, universities around its usage inside and outside the classroom, companies are determining how to integrate it into workflows, and in some cases countries are deciding whether to restrict or ban access (Stokel-Walker, 2023; Grose, 2023; Satariano, 2023) . Since its release rese","cbCaidUMOG3YAl7W","https://ap.wps.com/l/cbCaidUMOG3YAl7W","pdf",281571,46,"English","# Abstract\n# Introduction\n# Literature review\n## ChatGPT Background\n## LLMs and engineering education context\n# Methodology and data collection\n# Results and comparison of model performance\n# Discussion and future research directions","[{\"question\":\"What is the main goal of this study on ChatGPT in mechanical engineering?\",\"answer\":\"To examine how ChatGPT performs on mechanical engineering questions and to identify classroom and professional use cases and pitfalls for generative AI in this domain.\"},{\"question\":\"How did the researchers test ChatGPT’s performance?\",\"answer\":\"They presented ChatGPT with question sets from junior and senior mechanical engineering exams from a private university, along with practice questions for the Fundamentals of Engineering Exam (FE) in Mechanical Engineering.\"},{\"question\":\"How did GPT-4 compare with the free ChatGPT version?\",\"answer\":\"The GPT-4 subscription model significantly outperformed the free version, achieving 76% correct answers versus 51% for the free model.\"},{\"question\":\"Can ChatGPT likely pass the FE exam based on the findings?\",\"answer\":\"The study concludes that due to text-only input limitations and patterns of inconsistency and confident incorrect answers, neither model is likely to pass the FE exam, and the tool is best suited for users with expert knowledge.\"}]","ChatGPT & Mechanical Engineering - Examining performance on the FE Mechanical Engineering and Undergraduate Exams | PDF",116]