In the rapidly evolving landscape of education, digital technologies have
repeatedly disrupted traditional pedagogical methods. This paper explores the
latest of these disruptions: the potential integration of large language models
(LLMs) and chatbots into graduate engineering education. We begin by tracing
historical and technological disruptions to provide context and then introduce
key terms such as machine learning and deep learning and the underlying
mechanisms of recent advancements, namely attention/transformer models and
graphics processing units. The heart of our investigation lies in the
application of an LLM-based chatbot in a graduate fluid mechanics course. We
developed a question bank from the course material and assessed the chatbot's
ability to provide accurate, insightful responses. The results are encouraging,
demonstrating not only the bot's ability to effectively answer complex
questions but also the potential advantages of chatbot usage in the classroom,
such as the promotion of self-paced learning, the provision of instantaneous
feedback, and the reduction of instructors' workload. The study also examines
the transformative effect of intelligent prompting on enhancing the chatbot's
performance. Furthermore, we demonstrate how powerful plugins like Wolfram
Alpha for mathematical problem-solving and code interpretation can
significantly extend the chatbot's capabilities, transforming it into a
comprehensive educational tool. While acknowledging the challenges and ethical
implications surrounding the use of such AI models in education, we advocate
for a balanced approach. The use of LLMs and chatbots in graduate education can
be greatly beneficial but requires ongoing evaluation and adaptation to ensure
ethical and efficient use.

该研究论文探讨了大语言模型和聊天机器人结合应用于研究生工程教育的可能性，并通过一个研究案例证明了聊天机器人在课堂上的潜在优势，包括促进自主学习、提供即时反馈以及减轻教师负担。该研究还讨论了智能提示对聊天机器人性能的提升效果以及如何通过插件扩展聊天机器人的功能，同时强调在教育中使用这些人工智能模型所面临的挑战和伦理问题，并主张采取平衡的方法进行评估和改进。