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As large language models (LLMs) become more integrated into daily life, it is crucial to foster AI literacy among high school students. However, most AI courses target college-level learners and assume prior knowledge, while high schools often lack the foundational curriculum and infrastructure for traditional LLM education. To bridge this gap, we present a hackathon-based framework that makes LLM learning accessible, engaging, and hands-on. The program combines interactive lectures on core LLM concepts with a guided competition where students fine-tune models and build real-world applications, such as healthcare chatbots. This approach boosts motivation, programming skills, and practical understanding. Post-hackathon survey results show students gained both functional LLM experience and foundational knowledge. Furthermore, our framework can be extended to broader audiences, including learners without prior AI/NLP experience, offering a rapid, application-driven introduction to LLMs.

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Riding on the Back of a Whale: A Hackathon Framework for Introducing High School Students to Large Language Models

  • 会议论文

  • 发表日期：

    2025年

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  • Duc Nguyen,
  • Dong Le,
  • Long Nguyen,
  • Quyen Vu,
  • Tran Le,
  • Dung Nguyen,
  • Nga Huynh,
  • Huong Nguyen,
  • Phat Tran,
  • Dang Le,
  • Sang Truong,
  • Sanmi Koyejo,
  • Cuong Le,
  • Tho Quan

摘要

As large language models (LLMs) become more integrated into daily life, it is crucial to foster AI literacy among high school students. However, most AI courses target college-level learners and assume prior knowledge, while high schools often lack the foundational curriculum and infrastructure for traditional LLM education. To bridge this gap, we present a hackathon-based framework that makes LLM learning accessible, engaging, and hands-on. The program combines interactive lectures on core LLM concepts with a guided competition where students fine-tune models and build real-world applications, such as healthcare chatbots. This approach boosts motivation, programming skills, and practical understanding. Post-hackathon survey results show students gained both functional LLM experience and foundational knowledge. Furthermore, our framework can be extended to broader audiences, including learners without prior AI/NLP experience, offering a rapid, application-driven introduction to LLMs.