The short answer
Organizations can run a short workshop for leaders, builders, reviewers, frontline users, and administrators with role-specific scenarios and evidence of understanding.
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
AI literacy should change how people choose tools, protect data, verify outputs, disclose assistance, and escalate errors. One generic prompt class rarely covers those responsibilities.
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.
Completion is not competence. Training should be reinforced with approved tools, clear policies, practical exercises, and observation in the real workflow.
A practical way to do it
- Map each role to the AI decisions and risks it owns.
- Teach with realistic scenarios, prohibited-data examples, and failure cases.
- End with a scored exercise, named follow-up actions, and a refresh trigger.
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
Organizations can run a short workshop for leaders, builders, reviewers, frontline users, and administrators with role-specific scenarios and evidence of understanding.