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Computer Science > Artificial Intelligence

arXiv:2308.10454 (cs)
[Submitted on 21 Aug 2023]

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

Authors: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.
Subjects: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
Cite as: arXiv:2308.10454 [cs.AI]
  (or arXiv:2308.10454v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2308.10454
Journal reference: IJCAI2023 Symposium on Multimodal Reasoning with LLM

Submission history

From: Chen Cao [view email]
[v1] Mon, 21 Aug 2023 04:00:56 UTC (10,024 KB)
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