SlideChain: Semantic Provenance for Lecture Understanding via Blockchain Registration

TL;DR

SlideChain leverages blockchain technology to provide verifiable, tamper-evident provenance records for multimodal educational content extracted by vision-language models, enhancing trustworthiness and reproducibility in AI-generated instructional materials.

Contribution

This work introduces SlideChain, a novel blockchain-backed framework for semantic provenance, enabling verification and auditability of multimodal educational content at scale.

Findings

Pronounced cross-model semantic discrepancies in educational slides

SlideChain achieves tamper detection and reproducibility in semantic extraction

Evaluation of scalability and efficiency of blockchain-based provenance recording

Abstract

Modern vision--language models (VLMs) are increasingly used to interpret and generate educational content, yet their semantic outputs remain challenging to verify, reproduce, and audit over time. Inconsistencies across model families, inference settings, and computing environments undermine the reliability of AI-generated instructional material, particularly in high-stakes and quantitative STEM domains. This work introduces SlideChain, a blockchain-backed provenance framework designed to provide verifiable integrity for multimodal semantic extraction at scale. Using the SlideChain Slides Dataset-a curated corpus of 1,117 medical imaging lecture slides from a university course-we extract concepts and relational triples from four state-of-the-art VLMs and construct structured provenance records for every slide. SlideChain anchors cryptographic hashes of these records on a local EVM (Ethereum Virtual Machine)-compatible blockchain, providing tamper-evident auditability and persistent semantic baselines. Through the first systematic analysis of semantic disagreement, cross-model similarity, and lecture-level variability in multimodal educational content, we reveal pronounced cross-model discrepancies, including low concept overlap and near-zero agreement in relational triples on many slides. We further evaluate gas usage, throughput, and scalability under simulated deployment conditions, and demonstrate perfect tamper detection along with deterministic reproducibility across independent extraction runs. Together, these results show that SlideChain provides a practical and scalable step toward trustworthy, verifiable multimodal educational pipelines, supporting long-term auditability, reproducibility, and integrity for AI-assisted instructional systems.