A Survey on MLLMs in Education: Application and Future Directions

Weicheng Xing, Tianqing Zhu, Jenny Wang and Bo Liu ()
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Weicheng Xing: School of Computer Science, Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, NSW 2007, Australia
Tianqing Zhu: Faculty of Data Science, City University of Macau, Macau 999078, China
Jenny Wang: Australia Education Management Group, Melbourn 3001, Australia
Bo Liu: School of Computer Science, Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, NSW 2007, Australia

Future Internet, 2024, vol. 16, issue 12, 1-31

Abstract: This survey paper examines the applications, methodologies, and future prospects of multimodal large language models (MLLMs) within the educational landscape. MLLMs, which integrate multiple data modalities such as text, images, and audio, offer innovative solutions that enhance learning experiences across various educational domains, including language acquisition, STEM education, interactive content creation, and medical training. The paper highlights how MLLMs contribute to improved engagement, personalized learning paths, and enhanced comprehension by leveraging their ability to process and generate contextually relevant content. The key findings underscore the transformative potential of MLLMs in modern education, suggesting significant improvements in both learner outcomes and pedagogical strategies. The paper also explores emerging trends and technological advancements that could shape the future of education, advocating for continued research and collaboration among stakeholders to fully harness the capabilities of MLLMs. As the integration of MLLMs into educational settings progresses, addressing ethical considerations and ensuring equitable access remain critical to maximizing their benefits.

Keywords: multimodal large language models (MLLMs); AI in education; educational technology (EdTech); multimodal integration in learning; computer vision in education (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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