Unlocking Scientific Concepts: How Effective Are LLM-Generated Analogies for Student Understanding and Classroom Practice?

要旨

Teaching scientific concepts is essential but challenging, and analogies help students connect new concepts to familiar ideas. Advancements in large language models (LLMs) enable generating analogies, yet their effectiveness in education remains underexplored. In this paper, we first conducted a two-stage study involving high school students and teachers to assess the effectiveness of LLM-generated analogies in biology and physics through a controlled in-class test and a classroom field study. Test results suggested that LLM-generated analogies could enhance student understanding particularly in biology, but require teachers' guidance to prevent over-reliance and overconfidence. Classroom experiments suggested that teachers could refine LLM-generated analogies to their satisfaction and inspire new analogies from generated ones, encouraged by positive classroom feedback and homework performance boosts. Based on findings, we developed and evaluated a practical system to help teachers generate and refine teaching analogies. We discussed future directions for developing and evaluating LLM-supported teaching and learning by analogy.

著者
Zekai Shao
Fudan University, Shanghai, China
Siyu Yuan
Fudan University, Shanghai, Shanghai, China
Lin Gao
Fudan University, Shanghai, China
Yixuan He
Fudan University, Shanghai, Shanghai, China
Deqing Yang
Fudan University, Shanghai, Shanghai, China
Siming Chen
Fudan University, Shanghai, China
DOI

10.1145/3706598.3714313

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714313

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Classroom Technology

G302
7 件の発表
2025-04-29 23:10:00
2025-04-30 00:40:00
日本語まとめ
読み込み中…