The short answer
Learning teams get a review rubric for objectives, audience, evidence, misconceptions, practice, accessibility, assessment, and human accountability.
The decision standard is simple: preserve the source, state the limits, and make the next human check obvious. A useful article should reduce uncertainty without pretending that every unknown has been resolved.
What to examine
Training content can be factually plausible and still teach the wrong sequence, omit prerequisite knowledge, overload learners, or give an unsafe example.
Start with scope. Identify the product, account, audience, jurisdiction, data, and decision involved. Then separate what was directly observed from what a vendor, researcher, regulator, or commentator says. Record dates because AI products, access rules, and prices change quickly.
Do not treat fluent instructional copy as pedagogically sound. Subject-matter and learning-design review remain necessary.
A practical way to do it
- Define the learner, objective, prerequisite, and observable success behavior.
- Trace claims to approved sources and test examples for safety and inclusion.
- Pilot with representative learners and revise from errors, questions, and assessment results.
Keep the worksheet or test record with the draft. If another editor cannot reproduce the check from the saved evidence, the article is not ready.
Editorial guardrail
Do not fill a missing fact with a plausible sentence. Mark it as unknown, find a stronger source, narrow the claim, or remove it. Commentary belongs in a clearly labeled paragraph after the reported facts, not inside them.
Primary-source reading list
These are starting points, not automatic support for every sentence. The publishing editor must open each cited page and confirm the claim it supports on the day of review.
Bottom line
Learning teams get a review rubric for objectives, audience, evidence, misconceptions, practice, accessibility, assessment, and human accountability.