2025/09/14 by Yuheng Yang, Wenjia Jiang, Yang, Yuheng +9 · 1 voice · 4 citations
Computer Science · Decision Sciences · Social Sciences · #Expert finding and Q&A systems #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Multiagent Systems (cs.MA) #Scientific Computing and Data Management #Wikis in Education and Collaboration #cs.HC #cs.MA
paper · pdf · doi:10.48550/arxiv.2509.11062
openalex publication_date 2025/09/14 · arxiv published 2025/09/14 · openalex created_date 2025/10/18 · arxiv updated 2026/04/01 · openalex updated_date 2026/07/28
The rapid progress of large language models (LLMs) has opened new opportunities for education. While learners can interact with academic papers through LLM-powered dialogue, limitations still exist: the lack of structured organization and the heavy reliance on text can impede systematic understanding and engagement with complex concepts. To address these challenges, we propose Auto-Slides, an LLM-driven system that converts research papers into pedagogically structured, multimodal slides (e.g., diagrams and tables). Drawing on cognitive science, it creates a presentation-oriented narrative and allows iterative refinement via an interactive editor to better match learners' knowledge level and goals. Auto-Slides further incorporates verification and knowledge retrieval mechanisms to ensure accuracy and contextual completeness. Through extensive user studies, Auto-Slides demonstrates strong learner acceptance, improved structural support for understanding, and expert-validated gains in narrative quality compared with conventional LLM-based reading. Our contributions lie in designing a multi-agent framework for transforming academic papers into pedagogically optimized slides and introducing interactive customization for personalized learning.