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Detect AI Content in Articles

AI-generated news articles and editorial content are a growing concern for readers, editors, and publishers as language models become easier to deploy at scale.

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Checks news articles and editorial content

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Works with long-form articles

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Fast detection with no sign-up

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Private with content never stored

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AI-Generated Journalism: Detection, Trust, and Editorial Responsibility

The use of AI to generate news articles and editorial content has moved from a theoretical concern to a well-documented reality. Multiple major publishers including CNET, Sports Illustrated, and a number of local news groups have published AI-generated articles with minimal disclosure, some containing factual errors that human editors would have caught easily. Beyond large publishers, AI-generated content has been deployed at scale in content farms, political messaging operations, affiliate marketing sites masquerading as news, and spam networks designed to appear as legitimate journalism in search results and social media feeds. For editors, readers, and platform operators, the ability to quickly assess whether an article was AI-generated is now a practical media literacy skill rather than a niche technical concern, and the cost of failing to perform that assessment includes reputational damage, factual errors that mislead readers, and the gradual erosion of audience trust that affects every subsequent piece you publish.

Article-specific AI detection benefits from the fact that journalism has distinctive structural conventions that AI models adhere to very predictably. News articles written by AI almost always follow the inverted pyramid structure, open with a definitional or event statement, transition to background context using templated phrasing, and close with a generic conclusion that references unnamed experts or general implications. These structural patterns compound the statistical signals that general AI detectors measure, namely low perplexity and low burstiness, and the combined effect makes news-format AI text particularly detectable. Feature articles generated by AI tend to favour listicle structures, rely on publicly available background information without adding original reporting, and lack the specific quotes from named sources, on-the-record interview material, or scene-setting descriptions that require physical presence at the events being reported.

When the detector returns results on a news or editorial article, pay particular attention to whether high-scoring sections contain any original reporting elements. Named sources with direct quotes, independently verified statistics with current dates, on-the-record interview material, and physical descriptions of locations or events are things AI cannot generate authentically. Their absence in high-scoring sections is a meaningful editorial red flag beyond the statistical score alone, and their presence in low-scoring sections is reassurance that the content reflects genuine reporting rather than synthesis from training data. Combining the score with this structural review produces a more reliable verdict than either signal would on its own.

For editors and publishers, the workflow implications of AI article screening go beyond catching individual problematic submissions. A consistent screening practice across all incoming contributions establishes a quality bar that contributors quickly learn to anticipate, which deters AI submissions before they are made. Combining detection with process requirements, such as requesting source materials or interview recordings for high-scoring submissions, makes the system more robust against bypass attempts because process evidence is much harder to fabricate than statistical fingerprints. Over time, a publication that consistently applies these standards develops a contributor pool that produces genuinely original work, which is the long-term goal that detection should serve rather than an end in itself.

Associated Press Generative AI Use Policy Columbia Journalism Review: AI and News Authenticity

How to use this tool

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Paste the article text. The detector highlights AI-probable paragraphs so you can quickly identify sections written by AI versus human.

How It Works

Step-by-step guide to detect ai content in articles:

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Get the article text
Copy the full article body from the source document, submission system, web page, or email attachment. Exclude headlines, captions, and sidebar elements that follow different writing conventions from the main article body and may skew the analysis. The cleaner the input, the more meaningful the resulting score.
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Paste into the detector
Open FixTools AI Content Detector and paste the article text into the input field. For very long articles of three thousand words or more, consider also running individual sections separately to identify whether AI patterns are concentrated in specific parts of the article rather than spread evenly throughout.
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Review the AI analysis
Note the overall AI probability score and any sections highlighted as machine-generated. Read the highlighted sentences carefully and assess whether they contain original reporting elements or whether they read as generic synthesis of publicly available information. The texture of the flagged content often tells you more than the headline score.
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Act on results
Request revisions, ask for sourcing evidence such as interview recordings or contact details for quoted sources, or use the AI Text Humanizer to improve flagged sections if the article is your own work in revision. For contributor submissions, the standard editorial response is to ask the contributor to provide their source materials before proceeding further.

Real-world examples

Common situations where this approach makes a real difference:

News editor review

A digital news editor checking articles submitted by remote contributors runs each piece through the detector before assigning to a copy editor, treating any score above the team threshold as a trigger to request source materials before continuing with the editorial process and ensuring no AI-generated submissions make it into the publication pipeline.

Content aggregator audit

A content aggregator platform manager scans submitted articles for AI-generated content to enforce a human-written submission policy, runs nightly batch checks on the previous day's submissions, and uses the results to identify contributors who consistently submit high-scoring content for account review and possible removal from the platform.

Reader fact-check

A media-literate reader pastes a suspicious article into the detector when they encounter an unfamiliar publication or a piece with claims that seem too convenient, uses the result to inform how much further investigation the article warrants, and shares findings in community fact-checking forums to build collective awareness of AI content networks.

When to use this guide

Use this when reading or editing articles from unfamiliar sources, freelance contributors, syndicated content networks, or guest-post submissions where AI-generated journalism may be a concern and where authenticity affects an editorial or payment decision.

Pro tips

Get better results with these expert suggestions:

Check quotes and sources independently

AI-generated articles sometimes fabricate quotes or attribute statements to real people who never said them. After running the detector, verify the two or three most specific claims or quotes in high-scoring sections by searching for the quoted text or contacting the named source directly. Fabricated sourcing is a strong indicator of AI generation regardless of the detection score, and a single confirmed fabrication is editorially fatal even if the rest of the article would otherwise be acceptable.

Run the article without the headline

Headlines are often written separately from body copy and can skew the detection score because they follow different conventions than article body text. Paste just the article body for the most accurate analysis of the main content, and analyse the headline separately if you also want to assess whether it appears AI-generated. Keeping the two checks distinct produces cleaner signals.

Compare against the publication's style guide

AI articles often use vocabulary and structure that is generically article-like rather than specific to a publication's house style. If the submitted content does not match the publication's established voice, tone, or structural conventions, that stylistic inconsistency is a meaningful supplementary signal alongside the detection score. Pull a recent staff-written piece from the same publication and compare reading rhythm side by side.

Check author history before trusting moderate scores

A score of forty-five percent from a contributor who has published dozens of verified human-written articles is less concerning than the same score from a new contributor with no publication history or verifiable identity. Context and contributor track record matter when interpreting borderline results, and a moderate score from a trusted long-term contributor warrants a conversation rather than an immediate rejection.

Check byline consistency

Compare the article's writing style to other known work by the same byline. A sudden shift in quality or tone may indicate AI involvement even if the score is moderate.

Flag articles with no original reporting

AI-generated journalism often summarizes other sources without original quotes, data, or reporting. Combine the AI score with an editorial review for originality.

Run checks on contributed or syndicated content

Content from syndication networks, content farms, or guest contributors carries higher AI risk. Prioritize detection on these sources. Article-level AI detection helps publishers maintain editorial standards. For publishing teams managing freelance content, AI detection is one input alongside editorial review, fact-checking, and source verification. Editorial teams treat detection scores as one input rather than a binary judgment.

FAQ

Frequently asked questions

Paste the article body text into the FixTools AI Content Detector and review the resulting probability score along with the sentence-level highlights. Focus on sections with the highest scores and check whether those sections contain original reporting elements such as named sources, direct quotes from interviews, independently verified recent statistics, or descriptions of physical events. The absence of these elements in high-scoring sections is a meaningful editorial red flag, while their presence even in moderately scored sections is reassurance that the writer engaged in actual reporting work beyond synthesis from publicly available sources.

Yes, and it is well documented across multiple major publications and content networks. CNET, Sports Illustrated, and several large local news groups have published AI-generated articles, in some cases without clear disclosure to readers, and in some cases with factual errors that human editors would have caught. AI-generated content has also been deployed in disinformation campaigns, hyperlocal news farm operations, and content spam networks. Independent researchers tracking news aggregators have identified significant volumes of likely AI content appearing without disclosure, which makes detection a practical media literacy concern rather than a theoretical one.

The detector is optimised for English. Results for non-English articles are less reliable because the underlying statistical models are trained primarily on English text. For non-English content the tool may still provide some signal for languages with similar grammatical structures to English, such as Spanish or German, but accuracy will be meaningfully lower than for English-language analysis. For high-stakes assessment of non-English articles, use detection as a preliminary indicator and supplement it with editorial review by a native speaker who can identify generic phrasing patterns in the relevant language.

Run the AI detection check and document the score with a screenshot for your records. Then independently verify the specific claims using primary sources, search for the named individuals to confirm they exist, and attempt to contact quoted sources to confirm the quotes are real. If you believe the article contains deliberate AI-generated disinformation, report it to the platform where it appeared, share your findings with established fact-checking organisations such as PolitiFact or Snopes, and consider publishing your own analysis if you are in a position to do so. The combination of statistical detection and verification of specific claims is the strongest evidence base for calling out AI disinformation publicly.

The tool can only analyse text you have direct access to. If you have a paid subscription to a publication, copy the article text you can see and paste it into the detector. The tool cannot scrape, access, or retrieve paywalled content on your behalf, which is by design because automatic scraping of paywalled content would violate the terms of service of most publications and could expose users to legal risk. For research purposes, many academic institutions have licences that provide legitimate access to paywalled journalism.

Request supporting documentation: original interview notes, source emails, research bookmarks or browser history covering the research period, or earlier drafts with file metadata showing creation timestamps. Ask specific follow-up questions about the reporting process, the sources interviewed, and the context for specific factual claims in the article. A contributor who genuinely wrote the piece can typically answer these questions easily; a contributor who used AI typically struggles with specific follow-up because the model produced text without underlying knowledge. Document the conversation and the contributor's responses for your records.

Yes. Leading journalism organisations including the Associated Press and BBC have published AI content policies that define what AI assistance is acceptable, require disclosure of AI involvement in articles where it occurred, and prohibit publication of fully AI-generated content without human verification of facts and sources. An explicit, communicated policy is both an editorial standard and a deterrent against AI content submissions because it sets clear expectations and consequences. The policy should be visible to contributors before they submit work, included in contributor agreements, and applied consistently to build credibility.

The detector analyses the text itself rather than the process that produced it, so it cannot directly distinguish AI used for research from AI used for writing. A writer who uses AI to brainstorm ideas, gather background information, or summarise documents and then writes the final article in their own words will typically produce text that scores as human-written because the statistical patterns reflect human composition. A writer who uses AI to generate the actual draft and then lightly edits it will produce text that retains AI statistical fingerprints regardless of how much research preceded the generation step. The detector measures what is on the page, not what happened before it.

Most editorial workflows use a tiered approach. Scores below twenty percent are treated as acceptable without further action. Scores between twenty and fifty percent trigger a closer editorial read with attention to whether original reporting elements are present. Scores above fifty percent trigger a request for source materials or a conversation with the contributor before proceeding. Scores above eighty percent typically result in rejection unless the contributor can provide compelling process evidence. These thresholds vary by publication and by the type of content, with stricter thresholds applied to investigative or opinion pieces than to short news briefs.