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

arXiv: https://arxiv.org/abs/2409.16376 · HTML: https://mlsanity.com/paper/2409.16376/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 100 references that are in the map.
- First posted on arXiv 24 Sep 2024.

## Builds on (most cited first, 15 of 15)

- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [MusicLDM: Enhancing Novelty in Text-to-Music Generation Using Beat-Synchronous Mixup Strategies](https://mlsanity.com/paper/2308.01546/) (2023)
- [Evaluating Language Models for Mathematics through Interactions](https://mlsanity.com/paper/2306.01694/) (2023)
- [The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers](https://mlsanity.com/paper/2405.17739/) (2024)
- [Assessing AI Detectors in Identifying AI-Generated Code: Implications for Education](https://mlsanity.com/paper/2401.03676/) (2024)
- [A Large Language Model Approach to Educational Survey Feedback Analysis](https://mlsanity.com/paper/2309.17447/) (2023)
- [A Piece of Theatre: Investigating How Teachers Design LLM Chatbots to Assist Adolescent Cyberbullying Education](https://mlsanity.com/paper/2402.17456/) (2024)
- [How Can I Get It Right? Using GPT to Rephrase Incorrect Trainee Responses](https://mlsanity.com/paper/2405.00970/) (2024)
- [A Survey and Taxonomy of Adversarial Neural Networks for Text-to-Image Synthesis](https://mlsanity.com/paper/1910.09399/) (2019)
- [Automatic assessment of text-based responses in post-secondary education: A systematic review](https://mlsanity.com/paper/2308.16151/) (2023)
- [A Comprehensive Review of the Video-to-Text Problem](https://mlsanity.com/paper/2103.14785/) (2021)
- [Dimensions: A Competitor to Scopus and the Web of Science?](https://mlsanity.com/paper/1803.07525/) (2018)
- [More than Model Documentation: Uncovering Teachers' Bespoke Information Needs for Informed Classroom Integration of ChatGPT](https://mlsanity.com/paper/2309.14458/) (2023)
- [Vox Populi, Vox ChatGPT: Large Language Models, Education and Democracy](https://mlsanity.com/paper/2311.06207/) (2023)

## Built on by (most cited first, 6 of 6)

- [PhyEduVideo: A Benchmark for Evaluating Text-to-Video Models for Physics Education](https://mlsanity.com/paper/2601.00943/) (2026)
- [Seeing Sarcasm Through Different Eyes: Analyzing Multimodal Sarcasm Perception in Large Vision-Language Models](https://mlsanity.com/paper/2503.12149/) (2025)
- [Do Chains-of-Thoughts of Large Language Models Suffer from Hallucinations, Cognitive Biases, or Phobias in Bayesian Reasoning?](https://mlsanity.com/paper/2503.15268/) (2025)
- [The Use of Generative Artificial Intelligence for Upper Secondary Mathematics Education Through the Lens of Technology Acceptance](https://mlsanity.com/paper/2501.14779/) (2025)
- [Assessment of Evolving Large Language Models in Upper Secondary Mathematics](https://mlsanity.com/paper/2504.12347/) (2025)
- [Artificial Intelligence Competence of K-12 Students Shapes Their AI Risk Perception: A Co-occurrence Network Analysis](https://mlsanity.com/paper/2512.04115/) (2025)

## Abstract (arXiv)

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.