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arXiv · 2308.10454

Elucidating STEM Concepts through Generative AI: A Multi-modal Exploration of Analogical Reasoning

Chen Cao·Zijian Ding·Gyeong-Geon Lee·Jiajun Jiao·Jionghao Lin·Xiaoming Zhai
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Abstract

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.

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Chen Cao, Zijian Ding, Gyeong-Geon Lee, Jiajun Jiao, Jionghao Lin, Xiaoming Zhai. 2023-08-21. Elucidating STEM Concepts through Generative AI: A Multi-modal Exploration of Analogical Reasoning. https://arxiv.org/abs/2308.10454

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