Multimodality of AI for Education: Toward Artificial General Intelligence

Abstract

This paper addresses the growing importance of understanding how multimodal AGI can be integrated into educational practices. We first reviewed the theoretical foundations of multimodality in human learning, encompassing its concept and history, dual coding theory and multimedia theory, VARK multimodality, and multimodal assessment (section II-A). After that, we revisited the essential components of AGI, particularly focusing on the multimodal nature of AGI that distinguished itself from ANI. Based on its conversational functionality, multimodal AGI is considered an educational agent already tested in various educational situations (section II-B). And how significant text, image, audio, and video modalities are for education, the technological backgrounds of AGI for analyzing and generating them, and educational applications of AI for each modality were thoroughly reviewed (sections III-VI). Finally, we comprehensively investigated the ethics of AGI in education, originating from the ethics of AI and specified in three strands - (1) data privacy and ethical integrity, (2) explainability, transparency, and fairness, and (3) responsibility and decision-making. Four possible remedies for the ethical issues were suggested (section VII). The paper also discusses the implications of multimodal AGI for learning theories, current research gaps, and future directions (section VIII). 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.