用区块链记录课件语义提取过程，确保教育AI输出可验证可追溯。
SlideChain: Semantic Provenance for Lecture Understanding via Blockchain Registration
- 将多模态模型的语义提取结果哈希上链，构建不可篡改的溯源记录。
- 在1117张医学影像课件上发现不同模型间概念重合率低、关系三元组一致率趋近零。
- 实测证明系统能完全检测篡改，且多次运行结果一致，适合高可信教育场景。
现代视觉-语言模型（VLMs）被广泛用于解读与生成教育内容，但其语义输出难以验证、复现与审计。模型族、推理设置与计算环境差异削弱了人工智能生成教学材料的可靠性，尤其在高风险、量化要求高的STEM领域。本文提出SlideChain，一种基于区块链的溯源框架，用于大规模多模态语义提取的可验证完整性保障。利用自建的SlideChain Slides Dataset——包含1,117张大学医学影像课程幻灯片，从四个前沿VLMs中提取概念与关系三元组，并为每张幻灯片构建结构化溯源记录。这些记录的加密哈希锚定于本地EVM兼容区块链，实现防篡改审计与持久化的语义基准。首次系统分析了多模型间的语义分歧、跨模型相似性及讲义级变异性，揭示显著的模型间差异：许多幻灯片存在低概念重合度与近乎零的关系三元组一致性。进一步在模拟部署条件下评估了气体消耗、吞吐量与可扩展性，证实系统具备完美的篡改检测能力与独立提取运行下的确定性复现性。结果表明，SlideChain为可信、可验证的多模态教育流水线提供了实用且可扩展的解决方案，支持长期审计、复现与完整性，助力人工智能辅助教学系统的可信建设。
原文摘要 · Abstract (English)
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.
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