Beyond Text-to-Text: An Overview of Multimodal and Generative Artificial Intelligence for Education Using Topic Modeling
September 24, 2024 Β· Declared Dead Β· π ACM Symposium on Applied Computing
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Authors
Ville Heilala, Roberto Araya, Raija HΓ€mΓ€lΓ€inen
arXiv ID
2409.16376
Category
cs.AI: Artificial Intelligence
Cross-listed
cs.HC
Citations
14
Venue
ACM Symposium on Applied Computing
Last Checked
4 months ago
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.
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