Slide2Text: Leveraging LLMs for Personalized Textbook Generation from PowerPoint Presentations
Yizhou Zhou
TL;DR
This work tackles the challenge of converting PowerPoint presentations into personalized, high-quality textbooks at scale. It presents Slide2Text V3, a modular pipeline that uses OCR for content extraction, a multi-model LLM framework with prompt engineering, and Retrieval-Augmented Generation (RAG) from PDFs and the web to ground generated chapters. A JSON-driven data flow, FAISS vector storage, and a hybrid, weighted reference retrieval strategy enable grounded, customizable textbook generation, while a Flask-based UI and GitHub collaboration support practical deployment. The authors validate the system with a case study converting a 39-slide DX PPT into bilingual English/Japanese textbooks and assess it through a two-layer evaluation framework combining automated quality checks and pedagogical experiments, highlighting significant potential for streamlined, personalized education along with challenges in accuracy and interactivity that guide future work.
Abstract
The rapid advancements in Large Language Models (LLMs) have revolutionized educational technology, enabling innovative approaches to automated and personalized content creation. This paper introduces Slide2Text, a system that leverages LLMs to transform PowerPoint presentations into customized textbooks. By extracting slide content using OCR, organizing it into a coherent structure, and generating tailored materials such as explanations, exercises, and references, Slide2Text streamlines the textbook creation process. Flexible customization options further enhance its adaptability to diverse educational needs. The system highlights the potential of LLMs in modernizing textbook creation and improving educational accessibility. Future developments will explore multimedia inputs and advanced user customization features.