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Cited by 106Artificial Intelligence in EducationArtificial Intelligence in Healthcare and EducationArtificial Intelligence Applications

The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.

Performance of ChatGPT on the US fundamentals of engineering exam: Comprehensive assessment of proficiency and potential implications for professional environmental engineering practice

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Pursnani, Vinay, Sermet, Yusuf, Kurt, Musa, Demir, Ibrahim·2023·Computers and Education: Artificial Intelligence
DOI 10.1016/j.caeai.2023.100183
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Abstract

In recent years, advancements in artificial intelligence (AI) have led to the development of large language models like GPT-4, demonstrating potential applications in various fields, including education. This study investigates the feasibility and effectiveness of using ChatGPT, a GPT-4 based model, in achieving satisfactory performance on the Fundamentals of Engineering (FE) Environmental Exam. This study further shows a significant improvement in the model's accuracy when answering FE exam questions through noninvasive prompt modifications, substantiating the utility of prompt modification as a viable approach to enhance AI performance in educational contexts. Furthermore, the findings reflect remarkable improvements in mathematical capabilities across successive iterations of ChatGPT models, showcasing their potential in solving complex engineering problems. Our paper also explores future research directions, emphasizing the importance of addressing AI challenges in education, enhancing accessibility and inclusion for diverse student populations, and developing AI-resistant exam questions to maintain examination integrity. By evaluating the performance of ChatGPT in the context of the FE Environmental Exam, this study contributes valuable insights into the potential applications and limitations of large language models in educational settings. As AI continues to evolve, these findings offer a foundation for further research into the responsible and effective integration of AI models across various disciplines, ultimately optimizing the learning experience and improving student outcomes.

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Crossref · 10.1016/j.caeai.2023.100183

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Citations

Cites

Research works printed but not linked: 2

  1. Gilson, 2023, How does CHATGPT perform on the United States medical licensing examination? The implications of large language models for medical education and knowledge assessment, JMIR Medical Education, 9
  2. Hwang, 2023, Editorial position paper: Exploring the potential of generative artificial intelligence in education: Applications, challenges, and future research directions, Educational Technology & Society, 26

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Metadata

Authors
Pursnani, V., Sermet, Y., Kurt, M., & Demir, I.
Affiliations
University of Iowa (US)
Corresponding author
Sermet, Yusuf
ORCID
Venue
Computers and Education: Artificial Intelligence
Publisher
Elsevier BV
Published
2023
Volume
5
Pages
100183
ISSN
2666-920X (ISSN-L)
DOI
10.1016/j.caeai.2023.100183
OpenAlex
W4388490623
Cited by
106
References
33
Citations by year
2026: 13 · 2025: 34 · 2024: 50 · 2023: 9
FWCI
24.83
Type
journal article
Language
en
Open access
Licence
Locations
  • Computers and Education Artificial Intelligence (publishedVersion, cc-by-nc-nd)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • NOAA Institutional Repository (submittedVersion, cc-by-nc-nd)
  • arXiv (Cornell University) (cc-by)
Funders
  • National Science Foundation
  • National Oceanic and Atmospheric Administration
Topics
  • Social Sciences › Education › Artificial Intelligence in Education
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
Keywords
ChatGPT, large language models, Fundamentals of Engineering exam, environmental engineering education, artificial intelligence in education, exam question generation, mathematical problem solving, examination integrity, accessibility and inclusion

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