How to read an AI detection score and its highlights

What the number is measuring

A detector compares text against patterns it learned from large amounts of human writing and AI writing. The overall score reflects how strongly the text as a whole matches the AI side. It answers 'how machine-like does this read?' and not 'how many words came from a chatbot?' Keep that distinction in mind and most results become easier to interpret.

That difference matters. A score that leans AI doesn't mean a fixed share of the document was generated. A piece that is half human and half AI can produce quite different overall scores depending on how distinct each part is. Two essays with similar scores can also have arrived there in completely different ways. That's why the sentence view exists, and why it deserves most of your attention.

Reading the sentence highlights

In Winston AI, each sentence gets its own assessment, shown as a color:

• Red: the sentence reads as likely AI.
• Yellow: mixed signals. This often shows up where a person wrote the text and a tool polished or paraphrased it, or where someone edited AI output by hand.
• No highlight: the sentence reads as human.

Hover or tab to any sentence to see its individual AI probability. The pattern of colors usually tells you more than the overall score. A single red paragraph in an otherwise clean essay points to one section to ask about. Red spread evenly across a whole piece is a different story from a few yellow sentences in an introduction. Picture an essay whose quotations and personal examples are unhighlighted while its topic sentences and conclusion are red. That might mean the student wrote the core and used a tool to frame it. It's a question to ask, not an answer.

When a score deserves less weight

Some texts give any detector less to work with. A result on these is still worth reading, but it should carry less weight in your decision. Be more cautious when:

• The text is very short, such as a single paragraph or a few sentences.
• The genre is formulaic, like a lab report, a templated cover letter or a legal clause.
• The writer is working in a second language and writes in a careful, textbook style.
• The text has been through grammar or rewriting tools, which can push human writing toward yellow.
• The writer was told to follow a strict structure or model answer, so many submissions look alike.

For a fuller look at where results go wrong, read can AI detectors be wrong.

A low score isn't a guarantee either

Text that reads as human may still have had help from AI. Someone might use a chatbot for an outline and then write every sentence themselves, or heavily rework generated text. Detection looks at the writing on the page. It can't see the process behind it. That's why a detector works best as one input in a review, not the whole review. Your knowledge of the writer, the assignment and how the work was produced fills the gaps a score can't.

Turning a score into a next step

1. 1Run the scan in the AI detector and note both the overall score and where the highlights fall.
2. 2Re-read the highlighted sentences yourself. Do they share the habits you associate with AI writing?
3. 3Gather context: drafts, version history, earlier work, and the brief or prompt the writer was given.
4. 4If questions remain, talk to the writer and ask about the highlighted sections specifically.
5. 5Record what you found. Paid plans keep scans in your history and let you export a shareable PDF report. Teachers will find a full review routine on our page for teachers.