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.

翻译：大型语言模型（LLMs）的快速发展为教育领域带来了新的机遇。尽管学习者可通过基于LLM的对话与学术论文进行交互，但仍存在局限性：缺乏结构化组织且过度依赖文本，这阻碍了对复杂概念的系统性理解与深度参与。为应对这些挑战，我们提出Auto-Slides——一种基于LLM驱动的系统，可将研究论文转化为具有教学结构的多模态幻灯片（如图表和表格）。该系统借鉴认知科学原理，构建面向演示的叙事框架，并通过交互式编辑器支持迭代优化，以更好地适配学习者的知识水平与目标。Auto-Slides进一步集成了验证与知识检索机制，以确保内容的准确性与上下文完整性。通过大规模用户研究，Auto-Slides展现出强大的学习者接受度、对理解的结构化支持提升效果，以及相比传统基于LLM的阅读方式在叙事质量上的专家验证增益。我们的贡献在于设计了一个将学术论文转化为教学优化幻灯片的多智能体框架，并引入了面向个性化学习的交互式定制功能。