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.

翻译：在快速发展的教育领域中，数字技术不断颠覆传统的教学方法。本文探讨了最新的颠覆性趋势：将大型语言模型（LLMs）和聊天机器人整合到研究生工程教育中的潜在可能性。我们首先追溯历史与技术层面的颠覆性变革以提供背景，随后引入机器学习、深度学习等关键术语，并阐述近期进展的底层机制，即注意力/Transformer模型和图形处理单元。研究的核心在于将基于LLM的聊天机器人应用于一门研究生流体力学课程。我们根据课程内容构建了题库，评估了聊天机器人提供准确、有洞察力回答的能力。结果令人鼓舞，不仅展示了机器人有效回答复杂问题的能力，还揭示了在课堂上使用聊天机器人的潜在优势，例如促进自主学习、提供即时反馈以及减轻教师工作负担。本研究还考察了智能提示对提升聊天机器人性能的变革性作用。此外，我们展示了Wolfram Alpha等强大插件在数学问题求解和代码解释中的应用，如何显著扩展聊天机器人的功能，将其转化为一个全面的教育工具。在承认教育中使用此类AI模型相关的挑战和伦理影响的同时，我们主张采取平衡的方法。LLMs和聊天机器人在研究生教育中的应用可以带来巨大益处，但需要持续评估与调整，以确保其合乎伦理且高效地使用。