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.

本文介绍了使用基于GPT-4的ChatGPT模型进行工程基础考试的可行性和有效性，并通过非侵入式提示修改实现了模型的显著精度提升。该研究进一步探讨了复杂工程问题的解决方案，并强调了AI在教育领域中的挑战，如如何实现AI对不同种族和背景学生的包容性和无歧视性。

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

ChatGPT在美国工程基础考试中的表现：对熟练程度的全面评估和对环境工程实践的潜在影响