Generating high-quality, pedagogically useful questions from lecture slide decks is difficult because important instructional content is distributed across both text and visual elements, and because useful questions must be scaffolded across the flow of a presentation rather than generated slide by slide in isolation. This paper describes Slide Deck Q\&A Quality Assurance (slidesqaqa), a Flask-based software system that extracts text and rendered images from PDF slides and processes them through a four-stage large language model pipeline comprising window planning, deck synthesis, slide annotation, and reconciliation. The system reasons jointly about slide modality and pedagogical role, allocates bounded question budgets, and revises draft annotations at the deck level to reduce redundancy and improve coverage. The final output is a structured JSON annotation containing deck-level goals, section structure, slide-level summaries, question sets, and evaluation scores. Initial experiments on two technical lecture decks indicate that the pipeline can filter non-instructional slides and produce high-fidelity, pedagogically coherent questions for visually complex content. The working system is at https://slidesqaqa-974767694043.us-west1.run.app The software repository is at https://github.com/blinding2submit/slidesqaqa

翻译：从讲座幻灯片中生成高质量、具有教学意义的问题具有挑战性，因为重要的教学内容分布在文本和视觉元素中，且有效的问题必须根据演示流程进行阶梯式设计，而非孤立地逐张幻灯片生成。本文描述了幻灯片问答质量保证系统（slidesqaqa），这是一个基于Flask的软件系统，能够从PDF幻灯片中提取文本和渲染图像，并通过四阶段大语言模型流水线进行处理，包括窗口规划、演示文稿综合、幻灯片标注和整合。该系统联合分析幻灯片模态和教学角色，分配有限的问题预算，并在演示文稿层面修订草稿标注以减少冗余并提高覆盖率。最终输出包含演示文稿级目标、章节结构、幻灯片级摘要、问题集和评估分数的结构化JSON标注。在两个技术讲座幻灯片上的初步实验表明，该流水线能够过滤非教学幻灯片，并针对视觉复杂内容生成高保真、教学连贯的问题。工作系统位于https://slidesqaqa-974767694043.us-west1.run.app，软件仓库位于https://github.com/blinding2submit/slidesqaqa