Claude AI Detection Guide

What makes Claude-assisted writing worth reviewing

Claude can produce polished, careful prose that may read human at first glance. Reviewers should look for generic reasoning, overly balanced phrasing, weak source grounding, and style shifts from the writer's known work.

Start with the Claude detector, then compare results with AI detection methodology and how AI detection works.

Review workflow

Scan the document, inspect flagged passages, compare drafts or writing samples, and verify cited sources. If the result matters for school, publishing, or workplace decisions, document the evidence and allow a human explanation.

FAQ

Can a detector prove Claude wrote the text?

No. It can show Claude-like AI-writing risk, but exact model attribution requires more evidence.

What should be checked after a flag?

Check sources, author voice, revision history, and whether AI assistance was disclosed under the relevant policy.

Review checklist for model-specific detection

Use this guide as part of a broader writing-integrity workflow. Compare the detector score with the assignment brief, publication policy, author notes, draft history, citation quality, and the level of factual specificity in the text. A high-risk result should trigger review, not an automatic accusation.

Look for generic phrasing, repeated transitions, and claims without sources.

Ask the writer for context when the result affects grades, publication, or employment decisions.

Document the final decision and the evidence used to reach it.

Frequently asked questions

Can GPTZeroAI prove which model wrote a passage? No detector can prove model origin with certainty. The goal is to surface AI-likelihood signals and help reviewers decide what needs closer inspection.

Should teams rewrite text only to lower a score? No. Revisions should improve clarity, sourcing, examples, and accountability. GPTZeroAI should support responsible review rather than attempts to hide AI involvement.