Auto-Slides: An Interactive Multi-Agent System for Creating and Customizing Research Presentations
arXiv:2509.11062v3 Announce Type: replace-cross Abstract: 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 v
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Abstract: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.
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Subjects:
Human-Computer Interaction (cs.HC); Multiagent Systems (cs.MA)
Cite as: arXiv:2509.11062 [cs.HC]
(or arXiv:2509.11062v3 [cs.HC] for this version)
https://doi.org/10.48550/arXiv.2509.11062
arXiv-issued DOI via DataCite
Submission history
From: Yuheng Yang [view email] [v1] Sun, 14 Sep 2025 03:05:54 UTC (4,973 KB) [v2] Wed, 17 Sep 2025 11:06:49 UTC (4,973 KB) [v3] Wed, 1 Apr 2026 08:41:51 UTC (2,051 KB)
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