ai-hallucination-fact-check-protocol

To fact check AI generated text, this Skill applies a structured hallucination verification protocol that identifies claim types, reconstructs original sources, and guides source evaluation for citations, statistics, and factual assertions.

The SIFT method for AI hallucinations adapts traditional source evaluation by identifying specific claims within LLM outputs, then reconstructing and tracing those claims back to verifiable primary sources for classroom use.

You create a classroom verification activity by generating student exercises, teacher modelling scripts, and structured workflows that guide learners through evaluating AI-generated research summaries across various subject areas.

Common AI hallucinations include citation fabrication, statistical invention, misattribution of sources, and unsupported consensus claims, all of which require structured fact-checking protocols to detect and verify.

Yes, this fact checking protocol is designed for AI literacy instruction across subject areas, providing teachers with modelling guidance and verification workflows to evaluate AI outputs in diverse educational scenarios.

Verification protocols require manual source reconstruction and human evaluation to confirm AI claims, meaning the process provides structured workflows for educators rather than fully automated factual confirmation.