Elucidating STEM Concepts through Generative AI: A Multi-modal Exploration of Analogical Reasoning
This work addresses the problem of improving STEM education for learners by integrating AI, though it appears incremental as it applies existing AI methods to a new educational context.
This study tackled the challenge of making STEM concepts more accessible by developing a system that uses generative AI to create metaphors and visual representations, showing potential to enhance learning and engagement through an A/B/C test.
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.