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.

翻译：本研究探索将生成式人工智能（特别是大型语言模型）与多模态类比推理相结合，作为一种创新方法，以提升科学、技术、工程和数学（STEM）教育水平。我们开发了一个新颖的系统，利用生成式人工智能的能力，将数学、物理学和编程中的复杂原理转化为易于理解的隐喻。为进一步增强教育体验，这些隐喻随后被转化为视觉形式。本研究旨在通过使用视觉隐喻，提升学习者对STEM概念的理解及其学习参与度。我们通过随机A/B/C测试评估该系统的有效性，考察学习者的学习收获与动机变化。本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。研究结果将为教育系统的设计提供启示，以充分发挥人工智能赋能教育相关方的潜力。