  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.
