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Generative AI (GenAI) is transforming education by enabling personalized learning, yet most AI-generated content remains text-based, limiting engagement. Current models like ChatGPT and Claude explain concepts through text, which may hinder comprehension. This highlights the need for AI-driven video generation to enhance learning through dynamic, personalized visuals. This paper explores GenAI’s role in educational video generation, various text-to-video frameworks, and a multimodal approach for personalized animated content. Studies discuss AI-generated videos, while explaining the use of 3D CNNs and RNNs for temporal consistency. Jain et al. [1] highlight RIFE’s role in smooth transitions, and text-to-speech (TTS) integration further enhances comprehension. We identify gaps in real-time video synthesis and frame consistency, proposing a framework that integrates LLMs, text-to-image, TTS, and image-to-video models (e.g., Flux LoRA, Stable Diffusion, Runway Gen-1). Different techniques are used to help improve video quality, making AI-generated educational content more accurate and effective.

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Review of Text-to-Video Generation Methods for Educational Applications

  • 会议论文

  • 发表日期：

    2026年

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  • Ashwini Yadav,
  • Tanvi Yadwadkar,
  • Aditya Gundakalli,
  • Ramkishor Prajapati,
  • Mithra Venkatesan

摘要

Generative AI (GenAI) is transforming education by enabling personalized learning, yet most AI-generated content remains text-based, limiting engagement. Current models like ChatGPT and Claude explain concepts through text, which may hinder comprehension. This highlights the need for AI-driven video generation to enhance learning through dynamic, personalized visuals. This paper explores GenAI’s role in educational video generation, various text-to-video frameworks, and a multimodal approach for personalized animated content. Studies discuss AI-generated videos, while explaining the use of 3D CNNs and RNNs for temporal consistency. Jain et al. [1] highlight RIFE’s role in smooth transitions, and text-to-speech (TTS) integration further enhances comprehension. We identify gaps in real-time video synthesis and frame consistency, proposing a framework that integrates LLMs, text-to-image, TTS, and image-to-video models (e.g., Flux LoRA, Stable Diffusion, Runway Gen-1). Different techniques are used to help improve video quality, making AI-generated educational content more accurate and effective.