Across the web, AI-generated articles are increasingly indistinguishable at a glance from human-written ones, especially after a quick scan that focuses on surface fluency rather than substance.
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The incentives for producing AI-generated articles are strong and well understood: lower production costs, higher output volume, and the ability to fill content calendars or marketplace listings at scale without proportional growth in headcount. This economic logic has driven widespread use of AI article generation across freelance platforms, content farms, affiliate networks, and even some editorial organisations operating under cost pressure. For anyone in a position of receiving, evaluating, or publishing articles, including content buyers, editors, marketers, researchers, and informed readers, the ability to quickly assess authenticity is now a practical gatekeeping skill rather than a niche technical interest. An article that appears well-written and factually plausible on a quick read may still be entirely machine-generated with no original research, no first-hand reporting, no genuine expertise behind it, and potentially no factual accuracy beyond what the model regurgitates from its training data.
Detection tools assess articles using the same core methodology applied to shorter texts, but article-length content provides substantially more statistical data and therefore more reliable results. The two primary signals, perplexity which measures word choice predictability and burstiness which measures sentence length variation, are calculated across thousands of tokens in a full article, giving the classifier sufficient data to produce a high-confidence score. Article-specific patterns also include structural signals: AI-generated articles almost always follow a predictable hierarchical structure with predictable section lengths, use formulaic subheadings that pattern-match across topics, and exhibit consistent paragraph-level rhythm that deviates from the natural variation of human feature writing. These structural signals compound the per-sentence statistical signals to make article-length AI content particularly detectable when checked with appropriate tools.
When interpreting results on an article, consider the distribution of scores across sections rather than just the overall headline number. A well-written article that scores sixty percent overall but has two specific sections at ninety percent and the rest at thirty to forty percent suggests a writer who used AI selectively for difficult or research-intensive sections while writing the rest in their own voice. A uniformly high score across all sections suggests wholesale AI generation with minimal human intervention. Neither pattern is definitive without additional context, but both give you a practical basis for follow-up decisions about what additional verification or revision might be appropriate before relying on the article.
For editors, researchers, and publishers building ongoing relationships with contributors, article-level detection becomes most useful when integrated into a consistent workflow rather than applied as ad-hoc spot checking. A standard practice of running every submitted article through the detector establishes a quality bar that contributors learn to anticipate and write toward, which gradually shifts the contributor pool toward writers who produce authentic original work. The combination of consistent screening, clear standards communicated upfront, and willingness to engage in editorial conversation about flagged submissions produces better content over time than either pure detection or pure trust-based workflows would on their own.
Paste the article text. You will receive an overall AI probability score and section highlights showing which parts are most likely AI-generated.
Step-by-step guide to check if an article was written by ai:
Copy the article text
Select and copy the article body from the source document, content management system, or web page. Exclude the headline, byline, captions, and sidebar content because these elements follow different writing conventions from the main article body and can introduce noise into the statistical analysis if included in the same input.
Paste into the AI detector
Open the 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 so you can identify whether AI patterns are concentrated in particular parts of the article rather than spread evenly across the entire piece.
Receive the AI probability score
Review the overall score and highlighted sections to understand which parts of the article are most AI-probable. Read the highlighted passages carefully and assess whether they contain the original reporting elements that would distinguish genuine journalism from generic AI synthesis, such as named sources, direct quotes, or specific verifiable details.
Make an informed decision
Use the result to decide whether to publish, return for revision, investigate further with source verification requests, or reject the submission entirely. The detection score is one signal in a broader editorial assessment, and the right decision typically combines the score with your knowledge of the contributor, the topic, and the publication's standards.
Common situations where this approach makes a real difference:
Content buyer verification
A content buyer on a marketplace platform checks articles they received before approving and publishing them on their website. The two-minute pre-publication check has caught several AI-generated submissions over the course of a year, saving the buyer from publishing low-quality content that would have damaged audience trust and from paying for work that did not meet the brief specifications.
Media literacy research
A researcher studying the prevalence of AI content online samples articles from various news aggregators and checks each one for AI patterns. The aggregated data forms the basis for a research paper documenting the scale of AI-generated content in informational ecosystems and the patterns by which it spreads across different platforms.
Publisher contributor vetting
An online magazine editor checks articles submitted through an open contributor portal before forwarding to the editorial team for full review. The screening step has reduced the editorial team's workload by filtering out submissions that would have been rejected anyway, which lets the editors focus their attention on submissions with genuine potential rather than spending time identifying obvious AI content.
Use this when you encounter an article of uncertain origin, whether from a freelancer, a content service, a guest contributor, or an unfamiliar website, and want a quick data-driven answer before relying on the content for an editorial, financial, or research decision.
Get better results with these expert suggestions:
Look for articles that lack any specific dates or names
AI-generated articles often avoid specific names, dates, and places in body text because these require factual accuracy the model cannot reliably guarantee from its training data. An article with vague attribution such as some experts believe or recent studies show alongside no specific sourcing is a red flag that combines with a high detection score to suggest AI generation. Human-written articles typically include named sources, specific dates, and verifiable concrete details because human writers can fact-check these and incorporate them naturally.
Check whether the article could have been written in 2022
AI models have training cutoffs and produce content that is often temporally generic because the model cannot reliably distinguish current developments from historical ones. If an article on a current topic reads as if the most recent event it references is a year or two old, that staleness is a signal that AI may have generated the article from training data without supplementing with current research. Human writers covering current topics naturally reference recent developments because they read the news.
Run the lede and kicker separately
The opening paragraph or lede and the final paragraph or kicker of AI articles are typically the most structurally predictable sections because models default to formulaic opening and closing patterns. Paste just these two paragraphs to get a quick preliminary read before deciding whether to check the full article in detail. If the lede and kicker both score above eighty percent, the rest of the article is highly likely to be AI-generated as well, which can save you the time of a full analysis when triaging multiple submissions.
Ask for the original source materials
For articles submitted by contributors, request the sources, interview recordings or notes, or documents the writer drew on for their reporting. A human journalist can produce their source materials because they actually collected them as part of the reporting process. An AI-generated article typically references sources that either do not exist or were not actually consulted, which becomes apparent when the contributor cannot produce contact details, recordings, or notes. This process verification is more robust than detection alone because it is harder to fake.
Check the byline against the writing style
If a known author's article scores unusually high for AI, compare it to their earlier work. A sudden jump in writing quality or shift in voice is a secondary signal worth investigating.
Focus on transitions and conclusions
AI-generated articles often have the weakest originality in conclusion paragraphs, which tend to be generic summaries. Run these sections through separately if results are borderline.
Combine AI detection with originality search
Use the AI detector alongside the Plagiarism Checker to get a complete picture of an article's authenticity, including both whether it was AI-generated and whether it was copied.
More use-case guides for the same tool:
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