# Performance of ChatGPT on the US Fundamentals of Engineering Exam: Comprehensive Assessment of Proficiency and Potential Implications for Professional Environmental Engineering Practice

arXiv: https://arxiv.org/abs/2304.12198 · HTML: https://mlsanity.com/paper/2304.12198/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 36 references that are in the map.
- First posted on arXiv 20 Apr 2023.

## Builds on (most cited first, 5 of 5)

- [ChatGPT Participates in a Computer Science Exam](https://mlsanity.com/paper/2303.09461/) (2023)
- [Short-term Hourly Streamflow Prediction with Graph Convolutional GRU Networks](https://mlsanity.com/paper/2107.07039/) (2021)
- [Platform-Independent and Curriculum-Oriented Intelligent Assistant for Higher Education](https://mlsanity.com/paper/2302.09294/) (2023)
- [Realistic River Image Synthesis using Deep Generative Adversarial Networks](https://mlsanity.com/paper/2003.00826/) (2020)
- [DEM Super-Resolution with EfficientNetV2](https://mlsanity.com/paper/2109.09661/) (2021)

## Built on by (most cited first, 15 of 15)

- [The Effect of Sampling Temperature on Problem Solving in Large Language Models](https://mlsanity.com/paper/2402.05201/) (2024)
- [The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4](https://mlsanity.com/paper/2311.07361/) (2023)
- [Artificial Intelligence-Enabled Intelligent Assistant for Personalized and Adaptive Learning in Higher Education](https://mlsanity.com/paper/2309.10892/) (2023)
- [An Interdisciplinary Outlook on Large Language Models for Scientific Research](https://mlsanity.com/paper/2311.04929/) (2023)
- [Perception, performance, and detectability of conversational artificial intelligence across 32 university courses](https://mlsanity.com/paper/2305.13934/) (2023)
- [Integrating AI and Learning Analytics for Data-Driven Pedagogical Decisions and Personalized Interventions in Education](https://mlsanity.com/paper/2312.09548/) (2023)
- [An Empirical Evaluation of Large Language Models on Consumer Health Questions](https://mlsanity.com/paper/2501.00208/) (2024)
- [Evaluating ChatGPT-4 Vision on Brazil's National Undergraduate Computer Science Exam](https://mlsanity.com/paper/2406.09671/) (2024)
- [Professional Certification Benchmark Dataset: The First 500 Jobs For Large Language Models](https://mlsanity.com/paper/2305.05377/) (2023)
- [Speculative Exploration on the Concept of Artificial Agents Conducting Autonomous Research](https://mlsanity.com/paper/2312.03497/) (2023)
- [An Open-Source Dual-Loss Embedding Model for Semantic Retrieval in Higher Education](https://mlsanity.com/paper/2505.04916/) (2025)
- [Evaluating AI-Powered Learning Assistants in Engineering Higher Education: Student Engagement, Ethical Challenges, and Policy Implications](https://mlsanity.com/paper/2506.05699/) (2025)
- [ChatGPT & Mechanical Engineering: Examining performance on the FE Mechanical Engineering and Undergraduate Exams](https://mlsanity.com/paper/2309.15866/) (2023)
- [Evaluating AI Vocational Skills Through Professional Testing](https://mlsanity.com/paper/2312.10603/) (2023)
- [Integrating Generative AI in Hackathons: Opportunities, Challenges, and Educational Implications](https://mlsanity.com/paper/2401.17434/) (2024)

## Abstract (arXiv)

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.