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Anchor LLM inference provenance on-chain and require instructor co-signatures for high-stakes grading records

Design · All designs
Evidence · 1 claim (1 for) · 1 study (1 design), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description

To make AI-generated educational decisions auditable and attributable, the framework records not only the AI output but the full inference configuration on the consortium ledger: "the model release and inference configuration (model/checkpoint ID, prompt-template hash, decoding parameters, and random seed, etc.) as hashed records on a consortium blockchain". High-stakes records such as final grades and certificates carry both an AI service signature and an instructor co-signature, and any later override is appended as a linked, traceable transaction rather than overwriting history.

Design Implications

Context

Requirements

  • The AI service must sign its outputs and the smart contract must enforce the hybrid AI-plus-instructor signing protocol for important records

Constraints

  • Applies to key grading outcomes; low-risk tutoring interactions are not recorded on-chain

Target Learners

  • higher education students in online courses

Learning Goals

  • accountable and reproducible automated assessment

Claims

Examples

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Key Sources

  • Fuan Xiao, Jiahui Huang, Jia-Xin Huang, Huali Ren and Lang Li. (2026). Integrating LLM with consortium blockchain for personalized and verifiable online education in higher education. International Journal of Educational Technology in Higher Education. https://doi.org/10.1186/s41239-026-00618-5
Page metadata
Field Value
type design
id anchor-llm-inference-provenance-onchain-instructor-cosign
title Anchor LLM inference provenance on-chain and require instructor co-signatures for high-stakes grading records
description To make AI-generated educational decisions auditable and attributable, the framework records not only the AI output but the full inference configuration on the consortium ledger: "the model release and inference confi...
status review
generated by process:settle-candidates on 2026-10-15
trust unverified — no one has confirmed this page's content
sources 1 — fuan-xiao-2026

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