Slide Deck Q&A Quality Assurance App: A Multi-Stage Pipeline for Pedagogical Question Generation

Jim Salsman (2026) — arXiv preprint. cs.CL; cs.HC.

Overview

SlidesQAQA is a Flask-based system that extracts text and rendered images from PDF lecture slides and processes them through a four-stage llm pipeline: window planning (segment extraction), deck synthesis (cross-slide reasoning), slide annotation (per-slide question generation), and reconciliation (deck-level revision to reduce redundancy and improve coverage). The key innovation is joint reasoning about slide modality and pedagogical role, with a bounded question budget that forces prioritization of important content.

How It Works

Unlike earlier automated-question-generation systems that generate questions slide-by-slide in isolation, SlidesQAQA reasons across the entire presentation. This enables deck-level scaffolding — questions build on each other across the slide sequence, matching the intended instructional flow. The reconciliation stage filters non-instructional slides and revises draft annotations to eliminate redundancy, producing structured JSON output with deck-level goals, section structure, slide summaries, question sets, and evaluation scores.

Comparison to Related Systems

This approach contrasts with generate-then-validate-question-gen frameworks by front-loading pedagogical reasoning rather than post-hoc validation. Where ai-generated-slides-student-perception research has shown that AI-generated slide content can be perceived as lower quality, SlidesQAQA focuses on question quality rather than slide generation itself. It also differs from aissa-slides-analysis systems that analyze slides for accessibility rather than pedagogical question extraction.

Pedagogical Design

The bounded question budget per slide forces the system to make pedagogical decisions about what content merits a question — an implicit form of scaffolding that prioritizes key concepts. Initial experiments on two technical lecture decks demonstrated successful filtering of non-instructional slides and generation of pedagogically coherent questions for visually complex content. This has implications for formative-assessment automation at scale.

Implications for AIED

SlidesQAQA represents a practical step toward AI systems that understand pedagogical structure rather than just processing content. Its multimodal approach — jointly processing text and rendered images — addresses a known weakness of text-only question generation systems. For higher-ed instructors managing large courses, automated pedagogical question generation from existing slide materials could dramatically reduce the burden of creating formative assessments.

kt4eqg-personalized-question-generation — 7 of 8 papers in May 28 scan

automated-question-generation — the broader field this system contributes to

generate-then-validate-question-gen — an alternative pipeline architecture

ai-generated-slides-student-perception — student perspectives on AI-generated educational content

aissa-slides-analysis — complementary slide analysis for accessibility

scaffolding — the pedagogical principle embedded in the pipeline design

formative-assessment — the primary use case for generated questions

multimodal — the joint text+visual reasoning capability

higher-ed — the primary deployment context (lecture courses)

Citation