AI 中文总结
该研究针对现有AI演示排练工具缺乏幻灯片关联与迭代练习规划支持的问题，推出PolyPresentation多模态AI平台，经20次学术演示对比验证其能提供更优的演示改进支持。
AI 中文摘要
演示对于学生、研究人员和专业人员来说是说服性地传达想法的重要方式，但要有效完成演示需要反复练习，协调内容、表达、视觉材料和观众互动。现有的AI辅助排练工具可提供可扩展的反馈，但通常将演示视为单次完成的表演，对将反馈与幻灯片 deck关联或规划下一轮练习内容的支持有限。为解决这一差距，我们引入PolyPresentation，一种面向幻灯片感知迭代演示练习的多模态AI平台。PolyPresentation将逐幻灯片练习、完整排练、观众问答和反馈组织成统一的练习循环，利用基于幻灯片的证据帮助演示者诊断表现问题并为后续练习做准备。我们通过基于 rubric 的比较，在20次学术演示排练中将PolyPresentation与四个基线系统进行评估，还评估其与人工评分的一致性。结果表明，PolyPresentation能提供更具可操作性、上下文感知且面向练习的支持以改进演示，演示视频可在此URL获取。
英文摘要
Presentations are essential for students, researchers, and professionals to communicate ideas persuasively, yet delivering them effectively requires repeated practice that coordinates content, delivery, visual materials, and audience interaction. Existing AI-assisted rehearsal tools provide scalable feedback, but they often treat presentations as single-run delivery performances, offering limited support for linking feedback to the slide deck or planning what to practice in the next iteration. To address this gap, we introduce PolyPresentation, a multimodal AI platform for slide-aware iterative presentation practice. PolyPresentation organizes slide-by-slide practice, full rehearsal, audience Q&A, and feedback into a unified practice loop, using slide-grounded evidence to help presenters diagnose performance issues and prepare for subsequent practice. We evaluate PolyPresentation through a rubric-based comparison with four baseline systems on 20 academic presentation rehearsals, and additionally assess its alignment with human ratings. Results suggest that PolyPresentation provides more actionable, context-aware, and practice-oriented support for improving presentations. The demonstration video is available at https://youtu.be/MmWj9O_PJxw.
