Beyond Text-to-Text: An Overview of Multimodal and Generative Artificial Intelligence for Education Using Topic Modeling

TL;DR

This paper maps the research landscape of multimodal and generative AI in education, highlighting a focus on text-based models and identifying gaps in multimodal approaches through topic modeling of over 4000 articles.

Contribution

It provides a comprehensive overview of current research trends in multimodal generative AI for education using topic modeling, revealing underexplored modalities and guiding future research directions.

Findings

Predominant focus on text-to-text models in education

Underexploration of multimodal AI capabilities in educational research

Identified research gaps across different AI modalities and educational levels

Abstract

Generative artificial intelligence (GenAI) can reshape education and learning. While large language models (LLMs) like ChatGPT dominate current educational research, multimodal capabilities, such as text-to-speech and text-to-image, are less explored. This study uses topic modeling to map the research landscape of multimodal and generative AI in education. An extensive literature search using Dimensions yielded 4175 articles. Employing a topic modeling approach, latent topics were extracted, resulting in 38 interpretable topics organized into 14 thematic areas. Findings indicate a predominant focus on text-to-text models in educational contexts, with other modalities underexplored, overlooking the broader potential of multimodal approaches. The results suggest a research gap, stressing the importance of more balanced attention across different AI modalities and educational levels. In summary, this research provides an overview of current trends in generative AI for education, underlining opportunities for future exploration of multimodal technologies to fully realize the transformative potential of artificial intelligence in education.