Module 5 Summary: Check Claims Against Evidence

Consolidate the module decisions and keep the reusable resource available for later work.

What you can do now

You can inspect AI output systematically and decide whether it is suitable for a defined use.

The review sequence

1. Re-read the task specification.
2. Check accuracy, completeness, relevance, assumptions, visible reasoning, tone, and source quality.
3. Mark material claims and assumptions.
4. Verify important claims with suitable independent evidence.
5. Scale review depth to consequence, reversibility, reach, and detectability.
6. Record evidence, changes, owners, limitations, and the final decision.
7. Choose accept, revise, verify, or reject.

Main lessons

• Fluent writing is not proof.
• Correct included facts do not repair a material omission.
• A real citation may still fail to support or fit the claim.
• Asking the same model to confirm itself is not independent verification.
• High-impact work needs deeper review and qualified authority.
• Unresolved is better than an unsupported guess.
• Corrections must be reviewed again.

Before the knowledge check

The check has ten questions. You need 8 out of 10. Read the feedback before trying again.

Use the reusable Output Review Rubric and Evidence Log for future course work.

What comes next

Verification asks whether the output is supported. Responsible use also asks who may be affected, what information may be used, what attacks or misuse are possible, what rights questions exist, and when work must stop.

Module 6 covers these responsible and secure-use decisions.

Keep this rule:

Treat every material claim as unverified until suitable evidence supports it.