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.

翻译：近年来，人工智能（AI）的进步催生了GPT-4等大型语言模型的发展，其在教育等多个领域展现出潜在应用价值。本研究探讨了基于GPT-4的ChatGPT模型在美国基础工程（FE）环境工程考试中取得满意表现的可行性与有效性。通过非侵入式提示修改，本研究进一步表明该模型在回答FE考题时准确率显著提升，验证了提示修改作为增强AI在教育情境中性能的可行方法。此外，研究发现ChatGPT模型在连续迭代版本中数学能力显著提升，展现了其解决复杂工程问题的潜力。本文还探讨了未来研究方向，强调需应对AI在教育领域的挑战、提升不同学生群体的可及性与包容性，并开发抗AI考题以维护考试公正性。通过评估ChatGPT在FE环境工程考试中的表现，本研究为大型语言模型在教育场景中的应用潜力与局限性提供了重要见解。随着AI持续发展，这些发现为后续跨学科领域负责任且有效地整合AI模型的研究奠定了基础，最终有望优化学习体验并提升学生成果。