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Check If Content Is AI Written

Wondering whether a piece of writing actually came from the person who sent it? FixTools lets you check any text against the statistical patterns that language models leave behind, so you can tell within seconds whether the content shows the signatures of machine generation.

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Works on articles, essays, emails, and more

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No data stored or shared

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Highlights AI-like sentences

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No sign-up required

Cost: Free forever
Sign-up: Not required
Processing: In your browser
Privacy: Files stay local

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Add this AI Content Detector to your website

Drop the AI Content Detector into any page — blog post, product docs, intranet, school portal — with a single line of HTML. Your visitors get the full tool, processed entirely in their browser. No backend, no uploads, no signup.

Files stay 100% in the visitor's browser
Responsive — adapts to any container width
Free forever, no API key needed

Embed code

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Attribution-friendly: a small "Powered by FixTools" link appears in the embed footer.

Verifying Content Authenticity in a World Full of AI Writing

Trusting written content used to mean trusting the person who sent it. That equation broke the moment large language models became available to anyone with a web browser. A cover letter, a homework response, a freelance article, a customer email, a product review, a grant application: any of these can now be produced in seconds by a model that has read more text than any human author ever will. The question that used to sit quietly in the background of receiving written work, namely whether the words on the page reflect the person whose name is attached to them, has become a daily front of mind concern for managers, editors, instructors, recruiters, and anyone else who relies on written submissions for decision making.

The good news is that machine generation leaves measurable fingerprints. The most studied signal is perplexity, which captures how surprising each successive word is given the words before it. Models trained to predict the next token favor the most statistically likely continuation, so their output runs unusually smooth at the word level. Human writers regularly choose unexpected words, domain specific terms, idiosyncratic phrasings, or simple typos that boost perplexity. The second signal is burstiness, which measures variance in sentence length and structure. Humans naturally alternate between short clipped statements and longer winding explanations. Machine output tends to settle into a uniform medium length cadence that, once you start looking for it, becomes hard to unsee.

Interpreting the result is its own skill. A single overall score tells you something, but the sentence level highlights tell you more. A piece that scores 70 percent overall with two heavily flagged paragraphs and otherwise clean prose suggests a writer who plagiarized specific sections from a model rather than running the whole assignment through one. A piece that scores 70 percent uniformly suggests an end to end machine draft with light surface editing. The first calls for a conversation about the specific flagged passages. The second calls for a broader conversation about whether the work meets the standard you set. The detector gives you the data; your judgment turns it into a decision.

Privacy and access matter as much as accuracy. Many institutional detection systems require an enterprise license, a federated login, or an upload that lands on a vendor server. Those constraints make routine checking impossible for small teams, individual reviewers, and anyone working with sensitive material. FixTools runs the analysis in your browser, which means the content you paste never travels to a server, never gets stored in a database, and never becomes training data for someone elses model. You can check a confidential draft, an unpublished manuscript, or a private legal document with the same confidence as a public blog post, and you can do it from any device without setup or credentials.

On the Reliability of AI Text Detectors (MIT Technology Review)RAID: A Shared Benchmark for Robust Evaluation of AI-Generated Text Detectors (arXiv)

How to use this tool

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Paste the content you want to check. The tool highlights sentences with high AI probability and gives you an overall score.

How It Works

Step-by-step guide to check if content is ai written:

1
Open the AI Content Detector
Click the Open Tool button to launch the FixTools AI Content Detector in your browser. No login or account is required, and the tool loads in a couple of seconds even on slower connections. Keep the tab open as a standing utility while you work through your reviews.
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Paste the content you want to check
Copy the article, essay, email, message, or other written content and paste it into the input field. Plain text gives the most reliable results, so if your source is a formatted document consider pasting through a plain text scratch buffer first to strip hidden characters and markup.
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Get your AI probability score
Click the Check button to run the analysis. Within a few seconds the detector returns an overall AI probability percentage along with sentence level highlights showing which specific portions of the text most strongly match machine generated patterns. Read both pieces of information together.
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Decide how to proceed
Use the score and highlighted passages alongside your knowledge of the source to decide whether to approve the content, request revisions, ask the author to discuss specific sections, or escalate the matter through whatever review process applies in your context. The detector informs the decision but does not make it for you.

Real-world examples

Common situations where this approach makes a real difference:

Academic submission review

A teaching assistant for a 200 person introductory composition course processes weekly essay submissions ahead of the professors grading session. After noticing several papers with unusually polished prose he started pasting each submission into the detector, flagging anything above 60 percent for the professors closer review and noting the specific paragraphs the tool highlighted as suspicious. The process adds about 90 seconds per essay but has caught roughly a dozen clear cases this term.

Content marketing audit

A brand manager at a financial services firm runs quarterly audits on the content produced by her external SEO agency. The contract specifies original human writing and bans AI generated drafts. Each quarter she pastes ten randomly selected articles through the detector, documents the scores in a spreadsheet, and presents the results to the agency lead. The audit has changed the agencys staffing on her account and produced measurably more original content over time.

Job application screening

A hiring manager filling a remote analyst role asks each candidate to write a short response to a domain specific scenario. With several hundred applications coming in for the role she pastes every response into the detector during her first pass review, treating scores above 65 percent as a signal to weight the live interview more heavily on extemporaneous reasoning rather than written follow ups.

When to use this guide

Use this when you receive written content from an unknown or untrusted source and need a quick, objective assessment of whether it appears human-written before acting on it.

Pro tips

Get better results with these expert suggestions:

Check the opening paragraph first

Machine generated content almost always shows its most detectable patterns in the opening paragraph, because language models default to broad definitional or thesis style openings that pull from the most common training data conventions. A high score on just the first 100 words is often a strong early signal even before you read the rest. If the opening reads like the first paragraph of a generic Wikipedia article on the subject, run the detector on just those lines for a fast initial check that takes about five seconds.

Look for over-hedged language

Phrases like "it is important to note," "it is worth mentioning," "there are several key factors," "in conclusion," and "in todays digital landscape" appear at hugely disproportionate rates in language model output. They are linguistic comfort food the model reaches for when it does not have anything specific to say. If you spot three or four of these in a single document alongside a high detection score, the combined signal is much stronger than either piece of evidence alone, and you can treat the conclusion with more confidence.

Paste plain text only

Rich formatted content copied from Word, Google Docs, or web pages carries invisible characters, embedded markup, smart quote substitutions, and non breaking spaces that can subtly affect how the detector tokenizes the text. Run your sample through a plain text intermediate, such as a Notepad or TextEdit scratch buffer in plain mode, before pasting it into the detector. The cleaner input gives you a more stable score that reflects the language itself rather than the editing software the content traveled through.

Re-run after major edits

When you return content to a writer for revision and ask them to make it sound more human, run the detector on both versions and compare. A genuine human rewrite produces a meaningful score drop, often 20 to 40 percentage points, because the rewrite introduces real burstiness and word level variety. A cosmetic surface edit usually moves the score by less than 10 points, which tells you the underlying machine draft is still doing most of the work. The before and after comparison is a much stronger evidence pattern than any single score.

Compare against known human writing

If you have access to prior work by the same author, run both samples and compare scores to see if there is a significant difference in AI probability.

Look for uniform sentence length

AI-generated text often has very consistent sentence structure. Scan for sections where every sentence follows the same rhythm.

Check specific sections, not just the whole doc

Some writers use AI for parts of a document. Test individual paragraphs or sections separately to identify which portions may be AI-generated.

FAQ

Frequently asked questions

The detector handles any natural language prose: articles, blog posts, essays, emails, product descriptions, social media posts longer than a sentence or two, cover letters, reports, grant applications, customer support replies, and similar written content. It works best on prose paragraphs in English. Highly structured formats such as spreadsheet data, code listings, mathematical equations, or pure bullet lists are not meaningful inputs because the statistical patterns the classifier looks for are specific to flowing natural language rather than tabular or symbolic content.

No. The entire detection process runs in your browser, which means the text you paste never travels to a FixTools server and never gets stored, logged, or retained anywhere on our infrastructure. You can safely check confidential business documents, unpublished research, legal drafts, or sensitive personal writing without any privacy risk. Closing the browser tab clears the in memory copy completely, and refreshing the page leaves no trace of what you previously analyzed.

A score of 80 percent or higher is a strong signal that the text was generated by a language model. A score below 20 percent is consistent with original human writing. Scores between 20 and 80 percent fall into a genuinely ambiguous middle band, which can indicate AI assisted writing, heavily edited model output, formal academic prose, translated content, or a hybrid draft where some sections came from a model and others from a human. Mid range scores require additional judgment rather than a binary conclusion, and the sentence level highlights often tell you more than the overall number.

Yes, particularly when the original output has been substantially edited, paraphrased through a second model, or run through a dedicated humanizer tool that rewrites text specifically to evade detection. The detector works best on raw or lightly edited machine output, which is the most common form in circulation. Thorough human revision can reduce the statistical signal below detection thresholds, though the resulting writing usually shows other signs of mixed authorship that an attentive reader can spot through context and direct conversation with the author.

A plagiarism checker compares the submitted text against a database of existing published content and flags passages that match closely enough to indicate copying. An AI content detector analyzes the statistical properties of the text itself to determine whether its patterns match machine generation, without reference to any external source database. The two tools measure different things and answer different questions. Plagiarism checkers ask whether the writer copied from someone else. AI detectors ask whether the writer actually wrote the words at all. They are complementary rather than redundant.

It can detect AI patterns in shorter samples, but accuracy decreases substantially as the word count drops. Samples under 100 words give the statistical analysis too little material to work with reliably, and the scores can swing significantly based on a few unusual phrasings. For emails, try to include the full body text rather than just a sentence or two. For very short messages such as a one line reply, the detector is best treated as suggestive only and combined with other context such as the sender history and the conversation flow.

Use the score as a starting point rather than a final answer. A high score warrants a closer read of the specifically flagged sections and often a follow up conversation with the content source. For formal decisions such as academic integrity proceedings, contract disputes with freelancers, or editorial rejections, document the score with a screenshot, supplement it with at least one independent verification, and follow whatever internal process applies in your context. No single detection score should be the sole basis for a high stakes decision against a person.

Running the same passage twice should return very similar scores, typically within a few percentage points if any drift at all. Small variations can occur if the underlying classifier has been updated between runs or if you pasted slightly different versions of the text without realizing it, for instance by including or excluding a header. If you see large swings on what you believe is identical input, double check the paste, refresh the page, and try again with plain text only.

The detector requires loading the page once, which means an initial internet connection. After the page loads the analysis itself runs entirely in your browser without further network calls, so you can technically continue using the tool without connectivity as long as the tab stays open. For practical purposes treat it as an online tool that benefits from being available in any browser tab on any device with a network connection.

Like any classifier, the detector has known limitations. Formal academic prose, technical writing, translated content, and writing by non native English speakers can occasionally produce elevated scores because they share structural features with machine output: longer sentences, more conservative word choices, and a more uniform register. These are not failures so much as inherent limits of statistical detection. The practical response is to factor them in when reviewing borderline scores and to weigh the detection result alongside everything else you know about the writer.