🤖 AI Summary
This work addresses the challenge of generating high-quality, pedagogically coherent questions from multimodal lecture slides, which requires integrating dispersed textual and visual content while respecting the global instructional logic rather than treating slides in isolation. The authors propose a four-stage large language model pipeline—comprising window planning, lecture synthesis, slide annotation, and global coordination—that jointly models lecture-level teaching objectives and multimodal content for the first time. By incorporating quota allocation and cross-slide deduplication mechanisms, the system produces structured, low-redundancy question sets. Implemented in Flask, it supports PDF text and image extraction, multi-stage inference, and pedagogical evaluation. Experiments on two technical lecture datasets demonstrate its effectiveness in filtering non-instructional content and generating logically coherent, high-fidelity questions, along with comprehensive annotations including learning objectives, structure, summaries, and quality scores.
📝 Abstract
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