Auto-Slides: An Interactive Multi-Agent System for Creating and Customizing Research Presentations
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908929225654272 |
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| author | Yang, Yuheng Jiang, Wenjia Wang, Yang Song, Yi Wang, Yiwei Zhang, Chi |
| author_facet | Yang, Yuheng Jiang, Wenjia Wang, Yang Song, Yi Wang, Yiwei Zhang, Chi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_11062 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Auto-Slides: An Interactive Multi-Agent System for Creating and Customizing Research Presentations Yang, Yuheng Jiang, Wenjia Wang, Yang Song, Yi Wang, Yiwei Zhang, Chi Human-Computer Interaction Multiagent Systems 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. |
| title | Auto-Slides: An Interactive Multi-Agent System for Creating and Customizing Research Presentations |
| topic | Human-Computer Interaction Multiagent Systems |
| url | https://arxiv.org/abs/2509.11062 |