A Survey on MLLMs in Education: Application and Future Directions

Xing, W; Zhu, T; Wang, J; Liu, B

A Survey on MLLMs in Education: Application and Future Directions

Xing, W Zhu, T Wang, J Liu, B

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Future Internet, 2024, 16, (12)
Issue Date:: 2024-12-01

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Field	Value	Language
dc.contributor.author	Xing, W
dc.contributor.author	Zhu, T
dc.contributor.author	Wang, J
dc.contributor.author	Liu, B https://orcid.org/0000-0002-3603-6617
dc.date.accessioned	2025-01-16T04:41:18Z
dc.date.available	2025-01-16T04:41:18Z
dc.date.issued	2024-12-01
dc.identifier.citation	Future Internet, 2024, 16, (12)
dc.identifier.issn	1999-5903
dc.identifier.issn	1999-5903
dc.identifier.uri	http://hdl.handle.net/10453/183779
dc.description.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.
dc.language	English
dc.publisher	MDPI
dc.relation	http://purl.org/au-research/grants/arc/DP230100246
dc.relation	http://purl.org/au-research/grants/arc/LP220200808
dc.relation	http://purl.org/au-research/grants/arc/DP230100246
dc.relation.ispartof	Future Internet
dc.relation.isbasedon	10.3390/fi16120467
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	46 Information and computing sciences
dc.title	A Survey on MLLMs in Education: Application and Future Directions
dc.type	Journal Article
utslib.citation.volume	16
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-01-16T04:41:17Z
pubs.issue	12
pubs.publication-status	Published
pubs.volume	16
utslib.citation.issue	12

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.

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http://hdl.handle.net/10453/183779