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In the rapidly evolving landscape of education, digital technologies haverepeatedly disrupted traditional pedagogical methods. This paper explores thelatest of these disruptions: the potential integration of large language models(LLMs) and chatbots into graduate engineering education. We begin by tracinghistorical and technological disruptions to provide context and then introducekey terms such as machine learning and deep learning and the underlyingmechanisms of recent advancements, namely attention/transformer models andgraphics processing units. The heart of our investigation lies in theapplication of an LLM-based chatbot in a graduate fluid mechanics course. Wedeveloped a question bank from the course material and assessed the chatbot'sability to provide accurate, insightful responses. The results are encouraging,demonstrating not only the bot's ability to effectively answer complexquestions but also the potential advantages of chatbot usage in the classroom,such as the promotion of self-paced learning, the provision of instantaneousfeedback, and the reduction of instructors' workload. The study also examinesthe transformative effect of intelligent prompting on enhancing the chatbot'sperformance. Furthermore, we demonstrate how powerful plugins like WolframAlpha for mathematical problem-solving and code interpretation cansignificantly extend the chatbot's capabilities, transforming it into acomprehensive educational tool. While acknowledging the challenges and ethicalimplications surrounding the use of such AI models in education, we advocatefor a balanced approach. The use of LLMs and chatbots in graduate education canbe greatly beneficial but requires ongoing evaluation and adaptation to ensureethical and efficient use.

Beyond Traditional Teaching: The Potential of Large Language Models and Chatbots in Graduate Engineering Education