Multimodality of AI for Education: Toward Artificial General Intelligence

Abstract

This article addresses the growing importance of understanding how multimodal artificial general intelligence (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 (see Section II-A). After that, we revisited the essential components of AGI, particularly focusing on the multimodal nature of AGI that distinguished it from artificial narrow intelligence. Based on its conversational functionality, multimodal AGI is considered an educational agent already tested in various educational situations (see Section II-B). 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 artificial intelligence (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: first, data privacy and ethical integrity, second, explainability, transparency, and fairness, and third, responsibility and decision-making. Practical implementation of ethical AGI frameworks in education was reviewed (see Section VII). This article also discusses the implications for learning theories, derived operational design principles, current research gaps, practical constraints and institutional readiness, and future directions (see Section VIII). This exploration aims to provide an advanced understanding of the intersection between AI, multimodality, and education, setting a foundation for future research and development.