System converts school textbooks into personalized educational videos
Dynamic Learning Solutions: A System for Personalized Educational Video Generation
Artificial Intelligence
Summary
Many students learn better with videos, but making personalized educational videos is hard and time-consuming. The authors designed a system that takes a school textbook in PDF form and turns it into an interactive video explanation when a user asks a question. The system understands the textbook’s text and images, finds the relevant content, and creates a narrated animation with matching visuals to explain the topic. This approach helps turn static textbooks into fun, personalized learning videos.
What this means in practice
- •For educational content developers: Automatically create interactive videos from standard textbooks to enhance digital learning resources.
- •For digital learning platform engineers: Integrate personalized video explanations responding to user questions directly from uploaded course materials.$Commercial implications: Enables educational platforms to offer custom video content on demand, improving user engagement with scalable automation.
Authors
Siddhanth Sridhar, Shreya Chaurasia, Baddela Sai Yaswantha Reddy, Deepak Parmar, Shylaja S S
Abstract
We present an automated pipeline that converts NCERT textbooks into interactive video explanations that respond directly to user queries. A user uploads a PDF and asks a question; the system then generates a video-based explanation as output, handling both text and visual elements from the PDF for multi-modal retrieval and response generation. The pipeline combines a Retrieval-Augmented Generation (RAG) model with generative multimedia components. The RAG stage is optimized for the structure of NCERT textbooks and performs best on content from those books. Given a user query, the RAG model retrieves relevant content from the PDF and generates a multi-scene script containing narrative explanations and structured visual prompts aligned with the textbook's explanatory style. These prompts are passed to a Stable Diffusion module, implemented layer by layer for interpretability and control, which generates contextually relevant images. The images are then processed by DynamiCrafter to produce animated sequences. Finally, a Google Text-to-Speech module generates synchronized narration, aligning speech with the visual scenes through time-based control. The result is a coherent video explanation integrating animation, narration, and textbook-aligned visuals, transforming static educational material into an engaging learning experience. By combining multi-modal document retrieval, generative visual models, animation frameworks, and speech synthesis, this pipeline demonstrates a scalable approach to delivering interactive, personalized digital education content.