用主题模型梳理教育AI研究，发现多模态应用远未被充分探索。
Beyond Text-to-Text: An Overview of Multimodal and Generative Artificial Intelligence for Education Using Topic Modeling
- 通过主题建模分析4175篇文献，提炼出38个可解释的研究主题。
- 当前研究高度集中于文本生成，图像、语音等多模态应用严重不足。
- 适合关注AI教育未来方向的研究者和政策制定者阅读。
生成式人工智能（GenAI）有望重塑教育与学习方式。尽管大型语言模型（如ChatGPT）在教育研究中占据主导地位，但文本到语音、文本到图像等多模态能力仍被忽视。本研究利用主题建模方法，基于Dimensions数据库的4175篇文献，提取出38个可解释的主题，并归类为14个主题领域。结果显示，现有研究主要聚焦于文本到文本模型，其他模态的应用显著不足，暴露出研究空白。这凸显了需在不同人工智能模态及教育阶段间实现更均衡的关注。研究总结了当前生成式AI在教育领域的趋势，强调应进一步探索多模态技术，以充分发挥人工智能在教育中的变革潜力。
原文摘要 · Abstract (English)
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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