Educating future engineers about LLMs: A scalable workshop

Abstract: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: this https URL

Human-Computer Interaction (cs.HC); Computers and Society (cs.CY); Robotics (cs.RO)

arXiv:2610.07027 [cs.HC]

(or arXiv:2610.07027v1 [cs.HC] for this version)

https://doi.org/10.48550/arXiv.2610.07027 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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