A plagiarism check is a search. It compares your text against documents that already exist, and every match arrives with a source you can open and read for yourself. An AI check is an estimate. It reads the prose, judges how closely the writing resembles machine-written text, and returns a number with nothing behind it to click, because nothing was matched against anything. One kind of check produces evidence anyone can go and confirm. The other produces a reading about style. The two ask different questions, they fail in opposite directions, and institutions usually handle them under different rules, which is why conflating them produces the worst conversations. You can return a clean similarity report and still be accused of machine-written work, and you can be flagged for copying by a dropped quotation mark while your prose reads as plainly human. This site keeps the two questions apart on purpose. The originality check runs as a search and reports the passages it found with the pages they came from, and the AI checker returns a score from 0 to 100 with one of three readings: reads as AI-generated, borderline, or reads as human-written. This page sets the two checks side by side, explains what each percentage can and cannot support, and tells you which evidence to gather before you hand anything in.
A plagiarism check asks whether this text appears somewhere else, and where. It answers by matching strings from your document against an index of existing material: previously submitted papers, published pages, books and journals. The output is a list of matched passages, each with a location attached. Anyone can audit that output by opening the source and comparing the two paragraphs side by side.
An AI check asks how much the writing reads like machine-produced text. There is no index behind it and nothing to open. The output is one number, and the number describes the surface of the prose rather than its origin: even sentence lengths, predictable word choice, heavy connective scaffolding, a structure that follows a template. Those are properties of how a passage is built, not findings about who built it.
A similarity report is mostly a reading exercise. It shows the share of your submission that matched something in the index, with each matched run highlighted and linked. A high share is frequently innocent. Block quotations match their sources by design, reference lists match every other reference list that cites the same works, and standard phrasing in a discipline matches because it is standard. A marker who knows the tool opens the matches instead of judging the number.
What the report cannot see is wording that never appeared in a document before. Text generated on request is usually new wording, so a paper produced entirely by a model can come back with a very low similarity figure and no matches worth reading. That gap is exactly why AI checkers exist as a separate product category rather than as a feature of the older one.
An AI check has no index behind it, so there is never a source to click. That holds for every product in the category, and it is the most useful thing to understand before a score is put to you as evidence. It also means numbers from two different products are not comparable, because they come from different models trained on different material with different thresholds.
Our checker returns a score from 0 to 100 and one of three readings: reads as AI-generated, borderline, or reads as human-written. It needs at least 65 words, and one run costs one credit whatever the length. The free allowance is 10 credits a day per network, with no account, no login, no card and no email address. What the score responds to is the surface of the writing, and the free registry of 44 named AI writing tells highlights the exact phrases that push a score up, which turns a bare number into a list of sentences you can edit or defend.
Who gets caught by mistake is not random. Plain, even, correct prose is what these scores respond to, and that describes second-language writers and anyone taught to write to a template. Liang and colleagues at Stanford HAI ran TOEFL essays by non-native English speakers through seven detectors in 2023 and found 61 percent falsely flagged as AI-generated. Every one of those essays had a human author.
Plagiarism means presenting someone else's work as your own without credit. Generated text has no human author waiting to be credited, so under a strict reading of the word the answer is no. That is not a loophole, and it is not a defence. Academic codes commonly handle undisclosed AI use under a separate heading, usually unauthorised assistance or a breach of assessment conditions, and the penalties there can match the penalties for copying. The heading matters because it decides what evidence the institution has to produce.
The practical result is that the two outcomes come apart. You can return a clean similarity report and still have broken the rule that applies to your assignment, and you can be nowhere near a language model and still fail a similarity check because you dropped a quotation mark. On the policy itself, once: rewriting a draft you were permitted to generate is ordinary editing, and where AI is not permitted, that rule covers the draft however it is edited. Read the policy that applies to you.
A similarity false alarm clears quickly. You open the match, you see it is your own bibliography or a quotation you attributed properly, and the conversation ends. The evidence that raised the flag is the same evidence that removes it.
An AI false positive has no such exit. There is nothing to open and nothing to point at, so the writer is asked to prove a negative about their own habits. The writers who absorb this most often are the ones whose prose is plain and even by construction: second-language writers, people taught a rigid five-paragraph structure, people who write through dictation or a grammar checker. The only thing that reliably moves the conversation is process evidence, meaning version history, earlier drafts, notes and timestamps you did not create after the fact. The full argument, with what to say if it happens to you, is at why detectors flag human writing.
Start with the originality check, which keeps the two halves apart on purpose. Four panels run in your browser and read the document against itself: passages repeated inside your own draft, quotations with no attribution near them, citations that do not match the reference list in either direction, and figures or appeals to research left standing with no source in the same paragraph. A separate web check runs only when you press it, sends distinctive phrases to a search provider, and reports the passages it found with the pages they came from. Those are findings a marker can act on, and most of them are fixable in one editing pass.
Then run the checker for the separate style question, from 65 words up. If the score is higher than you expected, the AI vocabulary checker and the registry of AI writing tells will show you which specific phrases are driving it, free and unlimited in your browser. Words like 'delve', 'leverage' and 'moreover', and openers like 'it is worth noting', carry more weight than most writers expect.
Keep a record while you write rather than assembling one after an accusation. The drafting record signs your writing session with an ECDSA P-256 key generated in your browser, and a third party can verify the file in their own browser without contacting us. It shows when you started, how long you worked, how much you cut and rewrote, and whether anything large arrived in a single paste. That is process evidence, which is the kind that settles these conversations. The score itself is covered at what an AI score means.
Not as such. A plagiarism checker matches your wording against documents it already holds, and text generated on request is usually new wording that matches nothing. It will catch a model that reproduced a famous passage, or source material you pasted in and forgot to attribute, but a clean similarity report says nothing about whether a model wrote the draft. That is a separate check with separate evidence.
Not by the strict definition, because plagiarism means taking credit for another person's work and generated text has no author to credit. Institutions still prohibit undisclosed use, usually under a different heading such as unauthorised assistance or a breach of assessment conditions, and the penalty can be the same. The heading is worth knowing because it changes what has to be shown.
No, and any claim that it can is a confusion between two panels of the same report. An AI score is an estimate about how the writing reads. There is no matched document behind it, no link to open and no passage to compare. If someone quotes a score as proof of copying, ask them which number they are reading and ask to see the report itself.
It means nothing in your text matched anything in the index, and the style scored as machine-like. The two results are consistent with each other and with having written every word yourself. Ask which tool produced the AI figure, what threshold your institution treats as an accusation, and whether any evidence exists besides the number. Then bring your version history.
Because several products present both figures inside a single report, one panel next to the other, and the numbers then get quoted interchangeably in email. That is how a student ends up defending against copying when the actual claim was about style. If a percentage is put to you, the first question to ask is which of the two it is.