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.

翻译：生成式人工智能（GenAI）能够重塑教育与学习。尽管像ChatGPT这样的大型语言模型（LLM）主导了当前的教育研究，但文本到语音、文本到图像等多模态能力尚未得到充分探索。本研究采用主题建模方法，对教育领域多模态与生成式人工智能的研究图景进行系统性梳理。通过Dimensions数据库的广泛文献检索，共获得4175篇相关文献。运用主题建模方法提取潜在主题，最终归纳出38个可解释主题，并将其归类为14个主题领域。研究发现，当前教育情境下的研究主要集中于文本到文本模型，其他模态的探索相对不足，忽视了对多模态方法更广泛潜力的发掘。研究结果揭示了该领域存在的研究空白，强调需要更均衡地关注不同人工智能模态与教育层级。总之，本研究系统梳理了教育领域生成式人工智能的研究现状，强调了未来深入探索多模态技术对于充分实现人工智能教育变革潜力的重要意义。