This study explores the integration of generative artificial intelligence
(AI), specifically large language models, with multi-modal analogical reasoning
as an innovative approach to enhance science, technology, engineering, and
mathematics (STEM) education. We have developed a novel system that utilizes
the capacities of generative AI to transform intricate principles in
mathematics, physics, and programming into comprehensible metaphors. To further
augment the educational experience, these metaphors are subsequently converted
into visual form. Our study aims to enhance the learners' understanding of STEM
concepts and their learning engagement by using the visual metaphors. We
examine the efficacy of our system via a randomized A/B/C test, assessing
learning gains and motivation shifts among the learners. Our study demonstrates
the potential of applying large language models to educational practice on STEM
subjects. The results will shed light on the design of educational system in
terms of harnessing AI's potential to empower educational stakeholders.

本研究探讨了将生成式人工智能（AI）和多模态类比推理相结合，作为增强科学、技术、工程和数学（STEM）教育的创新方法。我们开发了一个新颖的系统，利用生成式 AI 的能力，将数学、物理和编程中复杂的原理转化为易于理解的隐喻。为了进一步增强学习体验，这些隐喻随后被转化为视觉形式。我们的研究旨在通过使用视觉隐喻来提高学习者对 STEM 概念的理解和学习参与度。通过随机的 A/B/C 测试，评估学习效果和学习者的动机变化，我们检验了我们的系统的效能。我们的研究证明了将大型语言模型应用于 STEM 学科教育实践的潜力。结果将为教育系统的设计提供利用人工智能的潜力来赋能教育相关方面的启示。