Multimodality of AI for Education: Towards Artificial General Intelligence
Gyeong-Geon Lee, Lehong Shi, Ehsan Latif, Yizhu Gao, Arne Bewersdorff, Matthew Nyaaba, Shuchen Guo, Zihao Wu, Zhengliang Liu, Hui Wang, Gengchen Mai, Tiaming Liu, Xiaoming Zhai
TL;DR
This paper surveys the multimodal AI landscape as a pathway to Artificial General Intelligence in education, emphasizing how integrating auditory, visual, kinesthetic, and linguistic modalities can transform teaching, learning, and assessment. It outlines foundational theories (Dual Coding, multimedia learning, VARK), surveys capabilities across text, graphics, and audio-visual domains, and discusses how generative models and multimodal agents—exemplified by LLMs like GPT-4 and multimodal systems like GPT-4V and KOSMOS-2—could support teachers and learners. The work also foregrounds ethical, explainable, and responsible use of educational AGI, addressing data bias, privacy, transparency, and human agency, and plans for governance, policy, and professional development. Collectively, the paper articulates a roadmap for integrating multimodal AGI into education to enhance accessibility, personalization, and learning outcomes, while acknowledging substantial technical and ethical challenges ahead.
Abstract
This paper presents a comprehensive examination of how multimodal artificial intelligence (AI) approaches are paving the way towards the realization of Artificial General Intelligence (AGI) in educational contexts. It scrutinizes the evolution and integration of AI in educational systems, emphasizing the crucial role of multimodality, which encompasses auditory, visual, kinesthetic, and linguistic modes of learning. This research delves deeply into the key facets of AGI, including cognitive frameworks, advanced knowledge representation, adaptive learning mechanisms, strategic planning, sophisticated language processing, and the integration of diverse multimodal data sources. It critically assesses AGI's transformative potential in reshaping educational paradigms, focusing on enhancing teaching and learning effectiveness, filling gaps in existing methodologies, and addressing ethical considerations and responsible usage of AGI in educational settings. The paper also discusses the implications of multimodal AI's role in education, offering insights into future directions and challenges in AGI development. This exploration aims to provide a nuanced understanding of the intersection between AI, multimodality, and education, setting a foundation for future research and development in AGI.