Free AI Detector and AI Checker: What It Catches and What It Misses
By Fırat Mıhcı. Built HumanizeMyAI on a 2,590-essay corpus. ResearchGate profile. Last updated July 2026.
TL;DR
Paste anything, no account needed, and this detector reads your text across 28 statistical signals from a model trained on 15,542 real human passages. It wrongly flags genuine writing under 0.2% of the time, and none of the non-native essays most likely to be falsely accused. Check your draft free.
What Does the AI Detector Report Show?
Every analysis gives you a complete, transparent breakdown of your content.
A single confidence score showing the likelihood your content was AI-generated, calculated across your entire text.
Overall confidence score
An AI content detector is a tool that estimates how likely a passage of text was written by a large language model rather than a person. It does that by measuring statistical fingerprints: how predictable the word choices are, how much the sentence lengths vary, and how often specific machine-favored phrasings show up. This detector is free and runs without an account. It does return a verdict, and it says so plainly when it does not have one. Read either as a signal, the way a single blood-pressure reading is a signal and not a diagnosis.
Most people land here for one of three reasons. You are a writer who wants to see whether your own draft reads as machine-written before someone else runs it through a paid checker. You are a teacher or an editor doing a quick first-pass spot-check. Or you are a non-native English writer whose genuinely original work keeps getting flagged, and you want to understand why, and what to do about it. This page is for all three, and the second half of it is mostly for the third.
What Does This AI Detector Measure?
Three families of signal feed every score, and none of them, on its own, is enough to call a passage machine-written. Together they produce a calibrated probability, a verdict that can be “inconclusive”, and a named-pattern list so you can see which phrasings a reader would notice in your draft.
Predictability is the classical signal, and it is worth understanding even though this detector deliberately does not use it. It measures how surprising each word is given the words before it. Language models are built to pick the most probable next word, so their output tends to be smooth, which reads as low perplexity. Human first drafts are noisier: people reach for an odd word, abandon a clause mid-thought, take a strange turn. We leave it out because of what happens to it under a rewrite. In our own testing, predictability collapses once a passage has been paraphrased, while sentence-rhythm variation barely shifts. A signal that disappears the moment someone edits their text is not one to build a verdict on.
Burstiness is the variance in sentence length and rhythm across a passage. Human writing is bursty: a four-word punch after a twenty-eight-word compound clause, then a short callback. Default model output varies far less, and that gap is the effect we keep coming back to, though we have not published a feature-importance breakdown for the model that ships: sentence-length variation separates human from machine prose better than any other feature we scored, and it survives matching the two sides for length, so it is not just long text against short. Burstiness is the signal researchers have leaned on since early 2023 , and it remains a core anchor in every credible detector.
A named-pattern list sits alongside the score, as explanation rather than as verdict. These are specific, recognisable tells, and they are worth knowing because once you can spot them in your own writing you can fix the ones that misfire on you: transition clustering (“moreover,” “furthermore,” “additionally” packed close together), three-verb parallel lists (“analyze, synthesize, and conclude”), generic importance language, list cadence (“firstly,” “secondly,” “lastly”), formulaic framing, vague attribution, and formal essay closers. Two tells you may expect are deliberately absent. The em-dash is not scored: we measured it at chance level, and our own study of model-specific habits identifies it as a Claude signature rather than an AI one. Paragraph uniformity is not scored either, because it turned out to separate document formats rather than authorship. Several of the patterns that remain trace to the lineage documented in Wikipedia’s editor guide on signs of AI writing; the rest came out of our own cross-checking against humanizer tools. The specific words and phrasings behind these patterns are collected in our AI words to avoid guide.
We publish the pattern categories on purpose. Transparency is the entire point of this detector against the opaque classifiers. The precise weights stay proprietary, but the thresholds do not: every scan hands back the two cuts your text was judged against and the word count that chose them, so you can see the line you were held to. That balance is deliberate. Publish nothing and the tool is a black box; publish the full recipe and any paraphraser scrubs it in a release cycle.
What this detector does not measure: it does not compare your text against the web or any source database, so it is not a plagiarism checker. It does not certify authorship. And it does not always commit. When your text falls between the calibrated human and AI thresholds for its length, the verdict comes back as inconclusive rather than as a guess in either direction. That is deliberate: on a question where a wrong answer costs a student a disciplinary hearing, abstaining is the more useful response.
How Do You Check If Text Is AI Generated?
Three steps, no account, no card.
- Paste your text. Drop any passage into the box above. Two things worth knowing before you paste. The 250-word cap applies to anonymous scans only: signing up is free and removes the word ceiling entirely, so a full essay goes through in one pass rather than in chunks. And if you came here looking for Turnitin’s Draft Coach, that is a separate product your institution either licenses or does not, checked inside Google Docs or Word rather than here; our Turnitin guide covers who actually gets it. Below 60 words the detector returns “too short to judge” rather than a verdict, because the rhythm signals it leans on need several sentences to stabilise. Longer passages are scored most reliably, so a full essay is the ideal length to check.
- Run the scan. The detector returns a calibrated probability in well under a second, plus the named-pattern list, which shows the specific phrasings it recognised in your text and quotes each one back to you.
- Read the breakdown, not just the headline number. This is the step people skip. A handful of patterns firing once each means something completely different from one pattern firing a dozen times across every paragraph. The first is a quick local fix; the second is a register issue. The pattern list quotes the matching phrase for each hit, so you can tell which one you have.
Checks are free and there is no signup gate: four scans a day of up to 250 words without an account, and 20 a day once you sign up. Paid plans raise that ceiling. The limits exist to stop scripted scraping, not to meter the tool.
How Accurate Is This AI Detector?
On our evaluation dated 30 July 2026, the detector scored 0.967 AUC, on the 0-to-1 scale detection research uses, where 0.5 is a coin toss and 1.0 is perfect, so 0.967 means it tells human writing from AI apart almost every time. What matters just as much if you write your own work: across 15,542 real human passages it wrongly flagged only 0.2%, and 0% of the non-native TOEFL essays and native student essays most likely to be falsely accused.
Plenty of tools advertise 99%. That figure is real only under one narrow condition: raw model output, in the register the detector was tuned for, with nothing done to it afterwards. It rarely survives contact with an edited draft, and it rarely comes with the number that makes accuracy meaningful: how often the same tool is wrong about a human. We publish both, on public benchmarks.
The table below shows exactly that: how often the detector wrongly flags real human writing, grouped by who wrote it, with every figure scored at the threshold the product actually ships.
| Who wrote it | Wrongly flagged | Passages | Kept out of training |
|---|---|---|---|
| Non-native writers, TOEFL essays (Liang et al. 2023) | 0% | 91 | yes |
| Native student essays, the matching control | 0% | 88 | yes |
| Literary prose | 0% | 2,500 | no |
| US student essays (ASAP-AES) | 0% | 2,515 | no |
| arXiv abstracts written before 2022 | 0.1% | 2,359 | no |
| Our own pre-ChatGPT student corpus | 0.2% | 7,378 | partly |
| Public long-form prose, news and web | 2.0% | 564 | no |
Across all of it, 34 of 15,542 human passages were flagged, a 0.2% false-positive rate, and zero on the ESL benchmark and its native-speaker control, the two groups most likely to be wrongly accused. Those are the numbers we lean on.
We can publish this because we also build the humanizer. A company that sold only detection would have every reason to round the inconvenient rows up. Every figure here comes from one dated run whose inputs are public benchmarks rather than examples of our own choosing: MAGE, HC3, ASAP-AES, Gutenberg, arXiv, and the Liang essays. You can read more about the research behind our corpus and the peer-reviewed work it draws on.
Can This AI Detector Catch Humanized AI Text?
Genuinely rewritten text reads as human. That is exactly what our humanizer is built to do. Run a draft through it and the machine fingerprints a detector reads are gone, so the text comes back reading like a person wrote it.
That is the point of the tool next to this one: paste AI-drafted text into our humanizer, get natural prose that reads human. A clean score afterward is the rewrite working.
Does This AI Detector Flag Non-Native English Writers?
No. In our own testing this detector flagged 0% of the 91 non-native TOEFL essays and 0% of the native-speaker control. Not flagging honest second-language writing is the case we tune for hardest.
If you write English as a second language and a detector has flagged your honest work, start here. The problem is real, it is documented in peer-reviewed research, and it is not your writing's fault. We keep a fuller resource on ESL writers flagged as AI if you want the research and the appeal options in one place.
In 2023, Weixin Liang and colleagues at Stanford published a study in the journal Patterns titled “GPT detectors are biased against non-native English writers” (DOI 10.1016/j.patter.2023.100779). They put seven commercial detectors in front of TOEFL essays whose authors were all non-native English writers. Those competitor detectors called 61.3% of them AI-generated, though a person had written every one. Held up against essays by native speakers, the same competitor detectors were wrong about 5% of the time.
The cause is structural, and once you see it the unfairness is obvious. The patterns mainstream detectors reward (formal transitions, parallel-list emphasis, hedged claims, even sentence rhythm, a controlled academic register) are exactly the patterns that formal English-as-a-second-language instruction teaches. TOEFL preparation drills produce, by design, the very register that reads as “machine-shaped” to a detector trained mostly on casual native-speaker prose. That was a structural weakness of the approach as it stood in 2023, not a quirk of one bad tool.
One thing worth saying plainly: the 61.3% figure that still circulates is a 2023 measurement and should be cited as one. Detection has broadly improved on this specific problem since then, and on the same 91 essays this detector returns no false positives at all. We keep testing on exactly those essays so the claim stays current, not frozen in 2023.
The 91 TOEFL passages Liang and colleagues published are part of our standing evaluation set, and the cut for each length band is taken from the worst of the human groups large enough to calibrate against rather than pooled across all of them. Pooling would have let our own student corpus set the line, which is comfortable for us and wrong for everyone else. On the Liang set the detector flags none of the 91, and none of the 88 in the native-speaker control, so both sides are measured rather than asserted. Read those zeroes with their width: at 91 passages a zero is consistent with a true rate as high as about 3%, and one group we measure separately, short question-and-answer prose, sits well above target at 12.8% because it is too small a sample to have set any cut. Every figure below is a July 2026 measurement:
| Writer profile | Measured false positives (July 2026) |
|---|---|
| Non-native English, TOEFL essays: never trained on (Liang et al. 2023 set, n=91) | 0% |
| Native English, student essays: never trained on (control, n=88) | 0% |
| Our pre-ChatGPT student corpus (overlaps training, n=7,378) | 0.2% |
| Public long-form human prose, news and web (overlaps training, n=564) | 2.0% |
More useful than the number is what you can do with the breakdown. When the detector flags your essay, open the named-pattern list. It will show you which specific patterns are firing, usually transition clustering, generic importance language, or low sentence-length variance, the standard fingerprints of a register you were explicitly taught. Seeing them named gives you two things: targeted edits that lower the score without changing your meaning, and evidence. If you are facing an institutional review over a detector flag, the Stanford paper is the canonical citation that no single detector score should be treated as proof, and your own version history plus the pattern breakdown documents that the writing developed over time. We cover the appeal path and the research in depth in the Stanford 2023 ESL detector-bias write-up.
What Does My AI Detection Score Mean?
The score is the headline, but the verdict and the named-pattern list are what you act on. The verdict is the model’s own call at a threshold calibrated for your text’s length; the pattern list is the part you can edit.
- Human. Your text reads as human-written. It sits below the model’s threshold for its length. No action needed.
- Inconclusive. The signals do not settle either way. This is common with formal academic prose and with polished non-native writing, which are more even and controlled than casual native-speaker text. It is not an accusation. Check the pattern list: if a couple of patterns fired once each, rewrite those phrases; if one fired repeatedly, the register itself is reading as uniformly formal.
- AI Detected. The score cleared the AI threshold for its length, a strong signal, though a detector score is never proof on its own. An unedited model draft usually lands here; an edited one often does not.
- Too short to judge. Under 60 words the detector declines to score rather than guess.
The named-pattern list is the difference between a useful tool and a number generator. It does not decide the verdict (the model does that), but it shows you which phrasings in your draft a careful reader would notice, and quotes each one back to you. That turns an anxiety-inducing percentage into a concrete edit list.
Is an AI Detector the Same as a Plagiarism Checker?
These three tools get confused constantly, and the confusion leads people to use the wrong one. They do different jobs.
An AI content detector (this page) estimates whether text was machine-generated, by reading its statistical style. It never looks outside the text itself.
A plagiarism checker estimates whether text was copied, by comparing it against the web and source databases. It says nothing about whether a human or a machine wrote it; a perfectly original AI-written essay passes a plagiarism check cleanly, and a hand-copied passage from a textbook fails it. For plagiarism, you want a tool built for matching, not detection. Some institutional tools run both at once, which is where the two get conflated; the SafeAssign AI checker guide walks through how a plagiarism-first system handles the AI question.
An AI humanizer does the opposite of a detector: it rewrites machine-drafted text so it reads more naturally and carries fewer of the tells a detector looks for. Our humanizer tool is the companion to this detector. Detect tells you what reads as machine-written, humanize rewrites it, and you re-check. A passage humanized with our tool may read clean here; that is the rewriter working, not proof of human authorship. The two tools are a loop, not competing verdicts, and the loop is covered in the last section.
One special case deserves its own note: Turnitin. Turnitin is the AI detector most students actually face, but it is institutional software. Instructors run it inside a learning-management system, and you generally cannot check your own work against it directly. Our general-purpose detector is a reasonable proxy for a pre-submission read, but it is not Turnitin and will not match Turnitin's number. If Turnitin specifically is your concern, including how its August 2025 detection update changed things, the dedicated Turnitin AI checker guide covers that in detail.
How to Read a Score Responsibly
It is not proof. It produces a probability, so treat a result as a signal to investigate, never as evidence on its own.
It is not a substitute for an institutional process. If you teach, use it as a first-pass filter to decide which submissions warrant a closer look, then cross-reference any flag against the student's version history and prior work before forming a view, especially for a non-native writer. A flag is the start of a conversation, not a conclusion.
Given all that, here is the workflow it is built for. The detector pairs naturally with the humanizer as a tight loop: paste raw AI-drafted text into the humanizer, which rewrites it against the 2,590-essay corpus; take the output and run it back through this detector; read the pattern list. If the verdict comes back Human with no pattern hits, the draft reads clean. If it is inconclusive, the list shows you the specific phrasings to rewrite by hand. The detector tells you what to fix, the humanizer fixes it, the detector confirms.
Detection here is free on purpose; a first-pass read is too important to put behind a paywall, so no scan costs anything and the daily limits exist only to stop scripted scraping. The humanizer carries the free-tier limits instead: a free account covers four rewrites of up to 250 words, with the paid plans (Basic $18/mo and up) lifting the cap for bulk or multi-page work. If you are still choosing a rewriter before settling on a workflow, the best AI humanizer comparison tests the field with real data. The cleanest path through all of it, in 2026, is still disclosure: most universities and most clients accept AI-assisted drafting when you say what you used, and a detector is one of the inputs that helps you decide what to disclose.
Does This AI Detector Earn Affiliate Commission?
HumanizeMyAI does not run paid placements or affiliate commissions on this detector or any tool comparison on this site. The detector accuracy data above is internal eval, the Liang et al. 2023 figures we compare against come straight from the published paper, whose 91-essay set is public so anyone can re-run our 0% on it, and the cross-detector recommendations are based on what we would actually use ourselves. If a competitor's tool wins on a specific dimension, we will say so. See the QuillBot humanizer review for an example of an honest comparison where we acknowledge specific strengths of a competitor's free tier.
The humanizer tool and the paid HumanizeMyAI plans (Basic $18/mo, Pro $27/mo, Ultra $48/mo) are the only commercial product surface on this site. The detector is free as a product decision: first-pass detection is too important to gate behind a paywall, and it costs us nothing to run: the trained model is a few thousand numbers evaluated locally, with no GPU and no API call.
Who Built This AI Detector?
Fırat Mıhcı built HumanizeMyAI on a corpus of real student writing. The detector, its topic-independent signal set, and the per-length calibration documented above are his work, and its feature choices come out of his own published studies on sentence rhythm, lexical register and semantic self-similarity. His academic background in second-language English research is why the non-native false-positive rate is measured on both sides rather than assumed. His ResearchGate profile, including the underlying ESL-writing research, is at ResearchGate · Fırat Mıhcı. Editorial review on this page was conducted internally in July 2026.
For questions about the detector methodology, false-positive incidents, or pattern-calibration suggestions, write hello@humanizemy.ai. For the institutional and faculty-facing detector workflow, see the Turnitin AI checker guide. For how to read the percentage itself, and why no vendor or university publishes an official acceptable score, see what percentage of AI is acceptable. For the companion humanization tool, see the humanizer. The full sub-cluster of detector and writing guides is linked in the Related grid below.
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