发表机构
Delft University of Technology（代尔夫特理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对工程硕士生设计了一个可扩展的游戏化工作坊，通过移动界面和真实机器人演示，训练学生与LLM协作进行导航规划，结果显示高学习收获和参与度，并观察到学生提示策略的演变。
AI 中文摘要
随着大型语言模型（LLMs）日益融入工程工作流程，学生需要动手实践经验来学习如何批判性地与它们协作。本文介绍了一个可扩展的游戏化工作坊，专为工程硕士生设计，以练习在导航规划中的人机协作。通过10次工作坊课程，使用移动网页界面，共226名学生为非推理型和推理型LLM编写提示词，以解决复杂度逐渐增加的基于网格的导航任务。系统返回机器人可执行的计划、轨迹可视化和自动评分，最终在波士顿动力公司（Boston Dynamics）的Spot机器人上进行现场演示。在工作坊后的问卷调查中，81.5%的学生报告有显著学习收获，91.0%的学生报告高参与度。对提交的提示词分析显示，学生从为非推理型LLM提供逐步指令的策略，转变为为复杂问题解决任务提供更高层次指导。我们得出结论，这种交互式仿真到现实的环境对于教授负责任地在工程中使用LLM所必需的验证和协作技能是可行的。代码可在以下网址获取：this https URL
英文摘要
As large language models (LLMs) are increasingly integrated into engineering workflows, students require hands-on experience to learn how to collaborate with them critically. This paper presents a scalable gamified workshop designed for engineering Master's students to practice human-AI collaboration in navigation planning. Using a mobile web interface across 10 workshop sessions, a total of 226 students wrote prompts for a non-reasoning and a reasoning LLM to solve grid-based navigation tasks of increasing complexity. The system returned robot-executable plans, trajectory visualizations, and automated scoring, culminating in a live demonstration on a Boston Dynamics Spot robot. In a post-workshop questionnaire, 81.5% reported substantial learning and 91.0% reported high engagement. Analysis of the submitted prompts revealed that students changed their strategies from step-by-step instructions for the non-reasoning LLM toward providing higher-level guidance for complex problem-solving tasks. We conclude that such interactive simulation-to-reality environments are viable for teaching the verification and collaboration skills necessary for responsible LLM use in engineering. Code is available at: https://github.com/renchizhhhh/LLM-robotics-workshop
