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In recent years, advancements in artificial intelligence (AI) have led to thedevelopment of large language models like GPT-4, demonstrating potentialapplications in various fields, including education. This study investigatesthe feasibility and effectiveness of using ChatGPT, a GPT-4 based model, inachieving satisfactory performance on the Fundamentals of Engineering (FE)Environmental Exam. This study further shows a significant improvement in themodel's accuracy when answering FE exam questions through noninvasive promptmodifications, substantiating the utility of prompt modification as a viableapproach to enhance AI performance in educational contexts. Furthermore, thefindings reflect remarkable improvements in mathematical capabilities acrosssuccessive iterations of ChatGPT models, showcasing their potential in solvingcomplex engineering problems. Our paper also explores future researchdirections, emphasizing the importance of addressing AI challenges ineducation, enhancing accessibility and inclusion for diverse studentpopulations, and developing AI-resistant exam questions to maintain examinationintegrity. By evaluating the performance of ChatGPT in the context of the FEEnvironmental Exam, this study contributes valuable insights into the potentialapplications and limitations of large language models in educational settings.As AI continues to evolve, these findings offer a foundation for furtherresearch into the responsible and effective integration of AI models acrossvarious disciplines, ultimately optimizing the learning experience andimproving student outcomes.

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