Free AI Detector for Students
Check a draft for AI before your institution does — and get the one thing detector sites leave out, which is what the number actually means.
Why Students Check Before Submitting
You wrote the essay yourself, but you have read enough stories about false flags to want to check before you submit. The detectors that will tell you are also the ones selling detection, and none of them will tell you how often they are wrong.
A Score You Can Interpret
Run the draft, see which sentences score high, and read the result against what detectors can actually measure. A high score is a prompt to look at specific sentences, not a verdict on your work.
How it works
- Paste or open your draft in the editor
- Run the AI check to get a score and a sentence-level breakdown
- Look at which sentences scored high rather than the headline number
- Rewrite those in your own voice, with your own sources and examples
- Re-check, then keep your drafts and version history either way
What the score means, and what it does not
A detector score is a probability estimate, not a measurement. No tool can see where text came from — there is no watermark, no metadata, nothing in the file that records authorship. Every score is inferred from the writing itself.
| The score says | The score does not say |
|---|---|
| This text is statistically similar to generated text | This text was generated |
| Word choices here are highly predictable | The writer did not think of them |
| Sentence structure is unusually even | A machine produced it |
| These specific sentences drive the score | Those sentences are wrong or bad |
This is why the sentence-level breakdown matters more than the headline percentage. A 46% score concentrated in two cautious paragraphs is a different situation from 46% spread evenly across the whole essay, and the number alone cannot tell them apart.
How accurate are AI detectors, really
Claimed accuracy and independent accuracy are different numbers, and almost every published comparison is run by a company that sells one of the tools. Treat the figures below as roughly indicative and note who produced them.
| Detector | Claimed accuracy | What independent testing reports |
|---|---|---|
| Turnitin | 98% on text over 300 words (vendor) | False-positive rates around 18% on ESL writing |
| GPTZero | 99%, validated by Penn State's AI research lab (vendor) | Scored far lower on at least one third-party benchmark |
| Originality.ai | 97% on its cited benchmark (vendor) | Accuracy falls sharply on edited rather than raw AI text |
| ZeroGPT | Not consistently published | Highest false-positive rate in several roundups; up to 28% on ESL writing |
The one figure that is properly peer-reviewed
Liang et al., published in Patterns in 2023, ran seven GPT detectors over 91 TOEFL essays written by non-native English speakers. The detectors wrongly flagged an average of 61.3% as AI-generated, and all seven unanimously flagged 19.8% of them. On essays by native-speaking US eighth-graders the same detectors were near-perfect.
Two things follow. A high score is weak evidence that text was generated, and a low score is weak evidence that it was not. Both are worth knowing before you read too much into either.
Why human writing gets flagged
Detectors score two properties, and ordinary careful writing can max out both without any machine involved.
- Predictable word choice. Academic registers are conventional by design. Writing the expected word in the expected place is what the genre asks for, and it is also what a model does.
- Even sentence structure. Writers who are less confident — in the subject, or in English — tend to produce sentences of similar length and shape. So do models.
- Writing in a second language. This is the single largest risk factor, for exactly the reasons above, and it is why the measured false-positive gap is so wide.
- Heavily edited drafts. Text revised many times tends toward the conventional, which reads to a detector the same way generation does.
- Technical and formulaic sections. Methods, definitions and literature summaries are constrained in how they can be phrased, and they flag often.
A counterintuitive finding
In the same study, simplifying the vocabulary of native-speaker essays increased how often they were flagged. Detectors are partly measuring vocabulary sophistication rather than authorship — which is why "write more simply" is bad advice for lowering a score, and why word-swapping tools are unreliable in both directions.
What to do with a high score
- Look at the sentences, not the number. Find which passages drive the score. That tells you something; the percentage on its own does not.
- Check whether those passages are vague. High-scoring sections are usually the ones where you wrote around a point instead of making it — introductions, conclusions, and anywhere you hedged.
- Add the specifics. Named sources, actual findings, your own examples. This lowers the score as a side effect of making the writing better.
- Do not word-swap. Replacing common words with rarer ones moves the number without improving anything, and the evidence suggests it is as likely to backfire as to help.
- Keep your drafts either way. Version history is the only real protection against a false flag, and it costs nothing to preserve.
If you have already been flagged on work you wrote yourself, the false positive guide covers what evidence to gather and how appeals usually work.
Want the method, not just the tool?
How to Humanize ChatGPT Text (Without Making It Worse) — Why AI text reads the way it does, how detectors actually work, why they flag innocent students — and the rewriting moves that make writing genuinely yours.
Try it on your own document
Write, cite, humanize and check your AI score in one editor. The free plan includes limited AI chat and AI detection each day, plus three humanizer runs — no card needed.
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