The short answer
Readers can assess an AI training program by the decisions and behaviors it prepares people to handle, not the number of prompt patterns it teaches.
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
Useful AI literacy includes human agency, ethics, foundational concepts, evidence, data protection, application skills, evaluation, disclosure, and knowing when not to use a system.
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.
Prompt fluency can increase risk if it is not paired with domain knowledge, verification, privacy, accessibility, and accountability.
A practical way to do it
- Define the decisions learners must make before, during, and after AI use.
- Teach concepts, ethics, data boundaries, evaluation, and escalation with realistic scenarios.
- Assess judgment and safe behavior, not memorized terminology alone.
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
Readers can assess an AI training program by the decisions and behaviors it prepares people to handle, not the number of prompt patterns it teaches.