AI Hallucination Fact-Check Protocol

What This Skill Does

Generates a fact-checking protocol specifically adapted for AI-generated text, extending the SIFT framework (Caulfield, 2019) with AI-specific moves that address the unique challenge of LLM hallucination. Standard lateral reading assumes a source has an institutional author whose funding and credibility can be investigated. This assumption breaks down for AI-generated text: there is no author to investigate, no institutional funding to check, no About Us page to scrutinise. What remains is the "Trace claims" move, and that move needs AI-specific calibration. AI hallucinations come in several forms: fabricated citations (a named study that does not exist, or exists but was never published), invented statistics (a number with plausible precision but no verifiable origin), real citations misattributed (a real paper attributed to the wrong author or journal), and false consensus claims ("most scientists agree" when no such consensus exists). Each requires a different verification move. The output includes a taxonomy of hallucination types for the subject area, an AI-adapted SIFT protocol, specific verification moves for each claim type, a Hallucination Hunt classroom activity, and a teacher modelling script showing the difference between finding a real and a fabricated citation.

Evidence Foundation