Some Examples of the “Track It Down” Move
Source: Mike Caulfield Substack
Author: Mike Caulfield
Original source: https://mikecaulfield.substack.com/p/some-examples-of-the-track-it-down
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Summary
Mike Caulfield demonstrates the “Track It Down” move by using NotebookLM to index roughly fifty walkthrough videos and surface examples where AI-generated statements needed verification. Many outputs were broadly right, but the process revealed errors, missing context, and better sources. The article shows that AI outputs can be useful starting points only when learners trace claims back to original or authoritative evidence.
Big ideas
- Students need to check AI answers against real evidence
- Students need to bring the purpose; AI should not supply it for them
Claims
- Students should check AI claims against trustworthy sources
- Research prompts can support inquiry without taking over student judgment
Key evidence and examples
- A Florida coastline example found that AI wrongly said a 1982 CBS report omitted a 25% flooding figure, while original footage showed the figure was mentioned with caveats.
- An Eiffel Tower example showed AI repeating a common 15 cm expansion claim, while the official site clarified the measurement differently.
- A Pope Francis and Cubs jersey claim became stronger when traced to Vatican News.
- An Emma Goldman quote was traced to Jack Frager’s 1973 T-shirt wording and a richer first-person account.
- Examples involving Mr. Rogers, lightning and rainbow photography, Einstein misattribution, and “hoedown” etymology show that source-tracing can confirm, correct, or deepen AI answers.
Education relevance
This is a strong classroom fit for AI literacy, lateral reading, media literacy, citation practice, and teaching students to move from generated answers to evidence.