Interpreting your score

The score is a density, not a probability

The 0–100 score measures how densely your text carries patterns typical of raw, unedited model output. It is not “the probability this was written by AI”, and not “the percentage written by AI”.

Higher means denser AI-style patterning — not “more likely to be AI”. A high score on formal or edited human writing is a known, explainable outcome (below), never proof.

Check the coverage line first

Each check refuses to judge when there isn’t enough text — you’ll see something like “9 of 11 checks had enough text to judge”. On short texts most checks abstain and the score rests on almost nothing, so treat anything under ~300 words as weak evidence, whatever the number says.

Confidence — how far to trust the read

Every result carries a confidence of high, moderate, or low, with a one-line reason. It is deliberately cautious: short text, or a score in the ambiguous middle (roughly 30–55 — “some patterns, but not clearly one way”), returns low — the same abstain-rather-than-guess rule the per-check signals follow. High means a decisive score across enough text with the trained model behind it — and still is not proof.

Text types

Formal writing reads “AI-ish” for reasons that have nothing to do with AI: few contractions, even sentence lengths, a careful register. So the scanner lets you set the text type, and it re-baselines — easing the formality signals for types where they would otherwise over-flag, instead of counting them against you. Each type below is its own setting, even where two share the same underlying register.

• Student essay / assignment — academic register; the formality patterns that over-flag careful coursework are relaxed.
• Research paper / article — the same academic register; dense citations and formal structure aren’t held against you.
• Blog / web article — expository web writing; a conversational-but-structured register.
• Creative / fiction — narrative voice; literary devices (aphorism, the reused simile) are expected there, not penalised.
• General (auto-detect) — the scanner infers the register from the text when you don’t pick one. Other document types — business writing, emails, cover letters, reports, technical docs — ride on this.

Picking the right type gives a fairer score; it never invents signs of AI writing that aren’t there.

Non-native & ESL writing — a separate thing

This isn’t a text type — it’s about who wrote it, and it cuts across every type above. Learned, careful English shares the surface habits of machine text (even sentences, formal word choice, few contractions), so non-native English is the most documented false-positive class across every detector — independent tests have measured other tools flagging non-native writers above 60%. ScanForAI eases exactly those formality signals whichever text type you’re in, so second-language writing isn’t punished for being careful. Choosing an academic type, or leaving auto-detect on, applies the same easing. Why non-native English gets flagged →

Known false positives

Human writing scores elevated when it is: formal or academic (few contractions, uniform sentences), written by non-native English speakers (the most documented harm across all detection tools — other tools have been measured falsely flagging non-native writers above 60%), heavily edited or house-styled, or simply short. If you’re contesting a score, open the per-signal breakdown: every claim this tool makes comes with its reason, which is the point.

Known false negatives

A light paraphrase or human editing pass removes most surface patterns. A low score means the text reads human — it cannot certify how it was made.

Never use this to accuse anyone