AI Detector for Academic Writing

What the score actually measures

The detector never sees the model that wrote your text, and there is no database of AI writing to check it against. Everything it reports is inferred from statistical properties of the words on the page.

Language models are prediction machines: at every position they pick from near the top of a ranked list of plausible next words. Do that a few hundred times and the result is fluent, coherent, and unusually predictable at every step. People do not write that way - we reach for a word that is slightly wrong and keep it, interrupt a clean sentence halfway through, and write a nine-word sentence after a forty-word one.

The engine measures that gap along several axes at once: how surprising each word is given the ones before it, how much sentence length varies, whether the text sits at a local probability peak rather than merely being likely, and about a hundred stylistic measurements covering vocabulary variety, sentence openers, repetition and punctuation habits.

It is trained specifically on academic writing - papers, reports and real student essays - which is the register most people paste into a tool like this, and where it performs at its best.

How to read the result

The percentage is evidence, not proof. It belongs in the same category as a partial fingerprint match: worth looking into, never sufficient on its own.

Two readouts travel with every scan to tell you how much weight the number deserves. Verdict stability re-scores your text under harmless surface variations and reports how far the result moved - a score that holds still is a property of your writing, while one that swings is an artifact of one particular arrangement of characters. Style familiarity measures how far your text sits from the writing the engine was trained on, so it can say when it is extrapolating rather than quietly guessing.

The sentence-level highlights are usually more informative than the headline number. They show which passages drove the result, which is what lets you judge whether the flagged stretches are the formulaic sections of an otherwise ordinary document.

Where it gets things wrong

No AI detector is 100% accurate - not this one, not any of them. Any tool claiming otherwise is selling you something. The honest failure modes are worth knowing before you rely on a result.

• Short text. Fewer sentences means less evidence, and short human writing genuinely resembles short AI writing. The bar is deliberately set higher on short passages, which means less gets flagged either way.
• Non-native English writers. Writing in a second language tends toward simpler vocabulary, more regular sentence structure and heavier use of learned connectives - statistically, that is also a description of the AI signal. This is the field's central fairness problem and it is tracked as a standing metric here, not treated as solved.
• Formulaic registers. Methods sections, lab reports and structured summaries are supposed to be uniform and predictable. That is the genre working correctly, and it reads as machine-like.
• Edited text. Writing that was generated and then substantially rewritten sits between the two classes, because it genuinely is between them.

Using it responsibly

If you are checking your own work, the useful question is not whether you are under some line. It is where the highlights fall and why. Long stretches of uniform, predictable, transition-heavy prose read as artificial partly because they are hard to read, and that is worth fixing regardless of whether any tool ever scans it.

If you are looking at a score about someone else's writing, a percentage is not a finding. Check the stability and familiarity readouts, look at whether the flagged spans are simply the formulaic sections, consider whether the author writes English as a second language, and treat the result as one input to a conversation rather than its conclusion. It should never be the sole basis for a decision about a person's work.

Common questions