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How do we cultivate genuine scientific inquiry in an age of automated answers? Led by STEM workforce development experts from Argonne National Laboratory, this hands-on session pairs an engineering challenge with Large Language Models (LLMs) to explore how we can prepare students for an AI-infused STEM workforce. Working in small groups, attendees will design a tabletop particle accelerator while moving between AI-supported and AI-free phases. This structure highlights when AI can augment human thinking, when humans must retain decision-making authority, and when human–AI collaboration can be most effective.
This breakout session at IASA's 62nd Annual Conference offers a practical example of how AI can support STEM learning while preserving student agency, encouraging meaningful inquiry, and making learning a natural consequence of hands-on investigation. Attendees will consider the conditions that make AI a valuable instructional support, and when its use may be inappropriate or less effective. Through reflection and discussion, they will examine how intentional approaches to AI can help students develop the critical thinking, problem-solving, collaboration, and decision-making skills needed for an AI-infused workforce.
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