AI Content Detector
Detect AI-generated content online for free. Check if text was written by ChatGPT or AI.
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How to Use This Tool
Paste the Text
Paste the text you want to analyse — an article, essay, email, or any written content.
Run Detection
Click Analyse. The AI detector analyses patterns in sentence structure, vocabulary, and phrasing to estimate the probability of AI authorship.
Review the Score
View the AI probability score (0–100%). Higher scores indicate AI-generated content. A breakdown highlights the most suspicious passages.
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Frequently Asked Questions
How accurate is AI detection?
Can AI detection produce false positives?
Which AI models can it detect?
What should I do if my human-written text scores high?
About AI Content Detector
A teacher receives a surprisingly well-written essay from a student whose previous work was two grade levels lower and wants a sanity check before a difficult conversation. A hiring manager reviewing a cover letter from a 'passionate storyteller' notices the third candidate in a row using the exact same cadence, em-dash pattern, and three-item lists — the unmistakable rhythm of a GPT-generated draft. This detector estimates the probability that a text was written by an AI language model using statistical features like perplexity (how predictable each word is given the preceding ones), burstiness (variation in sentence length and complexity), and token distribution patterns. It returns a percentage confidence with an honest caveat: AI detectors are statistical, not deterministic, and they get things wrong in both directions. Human-written formal prose (legal briefs, scientific papers, translated content) triggers false positives because formal writing is predictable by design. AI text that has been heavily edited passes as human because the statistical signature gets broken. Use the score as one signal, never as proof. Serious accusations of AI use require additional evidence — edit history, draft versions, process timelines, writing sample comparisons.
How it works
- 1
Perplexity and burstiness analysis
The detector computes statistical features of your text: perplexity (how predictable each word is given the preceding context — AI text tends to be lower-perplexity because it generates likely continuations) and burstiness (variation in sentence length and complexity — human writing is more varied than AI). These features are fed into a classifier trained on paired human-AI samples.
- 2
Token distribution and structural patterns
Beyond perplexity and burstiness, the classifier looks at token-level patterns: specific n-gram frequencies, transitional phrase usage ('Furthermore', 'Moreover', 'In conclusion' appear disproportionately in AI output), em-dash and semicolon density, and three-item list frequency. These signals are aggregated into a confidence score between 0 and 100.
- 3
Output is a probability, not a verdict
The score is a statistical estimate of how AI-like the text is, not a deterministic answer. A 70 percent score means the statistical features resemble AI output more than human output on average, not that the text was definitively AI-generated. Human formal writing often scores 40 to 60 percent; AI text heavily edited by a human often scores 20 to 40 percent. Use the score as one signal alongside other evidence.
Pro tips
Never treat a high score as proof of AI use
AI detectors produce false positives on human-written text that happens to share statistical features with AI output. Formal academic writing, legal briefs, technical documentation, and translated content (especially machine-translated-then-edited) routinely score above 70 percent while being entirely human. Non-native English writers often score high because their prose tends toward the grammatically correct and structurally predictable — exactly what detectors flag. Before accusing a student of academic misconduct or rejecting a candidate, gather additional evidence: draft history, process timeline, writing sample comparisons, direct conversation about the work. A detector score alone is never enough.
Heavily-edited AI text frequently passes detection
The inverse of the false-positive problem: AI text that a human has substantially rewritten — changed sentence structures, added personal anecdotes, varied rhythm, injected specific details — loses the statistical signature detectors look for. A student who runs AI output through 30 minutes of thoughtful editing will often score under 30 percent on every major detector including Turnitin, GPTZero, and Originality.ai. This is why AI detection as a sole enforcement mechanism is fundamentally limited; it catches lazy AI use and misses careful AI use. Educators should think about detecting AI use through assignment design (in-class writing, drafts-visible workflows) rather than relying on post-hoc detection alone.
Detector accuracy varies by content type and length
Detectors are most accurate on medium-length (300 to 1,000 word) general-topic prose. They lose accuracy on very short text (under 100 words has too few statistical samples), highly technical or domain-specific content, creative writing with intentional stylistic choices, and text that has been through multiple AI systems or heavy paraphrasing. Do not run a 50-word paragraph through a detector and trust the result. For serious determinations, work with longer samples and multiple detectors — compare GPTZero, Originality.ai, and Copyleaks results for a single text and look for consensus. Disagreement between detectors is a strong signal that the text is ambiguous.
Honest limitations
- · Statistical estimate, not definitive proof; false positives on human formal writing and false negatives on heavily-edited AI text are both common.
- · Short texts (under 100 words) produce unreliable scores because there are too few statistical samples to analyze.
- · Detectors trained on 2023-era AI output may be less accurate on newer models (GPT-4.5, Claude Opus 4, newer open-source models) as generation quality improves.
Frequently asked questions
How accurate is this detector compared to GPTZero or Originality.ai?
Our detector and commercial tools like GPTZero, Originality.ai, and Copyleaks all share fundamental limitations — they are statistical classifiers with inherent false positive and false negative rates. Published accuracy claims (90 percent, 99 percent) apply to specific test sets under controlled conditions and do not generalize to real-world use. Research like the Stanford HAI study on AI detector bias found false positive rates of 10 to 60 percent on human-written text from non-native speakers. Use our tool for preliminary screening, cross-reference with 1 or 2 other detectors for important decisions, and never rely on any single tool as sole evidence of AI use.
Why does my clearly human-written essay score as AI?
Several patterns trigger false positives: formal academic or business writing with consistent structure and vocabulary (AI favors this style), non-native English writing that tends toward grammatical correctness and predictable sentence structures, heavily-edited or copy-edited text where human quirks have been smoothed out, translated content where machine translation or a careful translator produces predictable phrasing, and topic-specific writing (legal, medical, technical) where specialized terminology is necessarily consistent. If your genuine writing scores high as AI, it is not because the detector has special insight — it is because your writing shares statistical features with AI output, which is unfair but currently unavoidable with purely statistical detection.
Can I trust this score to take disciplinary action against a student?
No. Formal disciplinary action for academic misconduct based solely on a detector score is not defensible under most university academic integrity policies and has been successfully challenged by students in multiple documented cases. Detectors are one signal, not proof. Before any action, gather corroborating evidence: compare the suspect submission against the student's previous work for style consistency, request draft history or require a verbal explanation of the reasoning, check for citation authenticity (AI often cites fake papers), and consider the student's language background and the writing context. A score above 80 percent is reasonable cause for conversation; it is not reasonable cause for a misconduct finding.
Does this work on text generated by newer AI models like GPT-5 or Claude Opus 4?
Partially. Detectors trained predominantly on GPT-3.5 and GPT-4 era output have reduced accuracy on newer models because generation quality has improved — newer models produce more varied sentence structure and vocabulary, reducing the statistical signatures that detectors rely on. This means false negative rates (missing actual AI content) are increasing as models improve. Detectors are in an arms race with generators and are fundamentally at a disadvantage because detection is harder than generation. Treat any detector's output on 2025-era or newer AI as a floor on detection capability, not a ceiling — actual AI use is probably higher than detectors suggest.
Should I submit text that contains sensitive information to this detector?
Consider carefully. Like our other AI-based tools, text is sent to our API which may call an AI provider for analysis. We do not log or retain input beyond the immediate request cycle. For genuinely sensitive content (student work subject to FERPA, employee material, confidential business documents), evaluate whether detection is worth the data exposure. Alternatives include on-premises detection tools (less accurate but zero data exposure), rough manual review using the heuristics this tool uses (look for predictable structure, clichéd transitions, three-item lists), or focusing detection resources on the subset of submissions where AI use is most suspected rather than running every document through an external service.
AI detection pairs with the other AI writing tools in a specific way. The ai-writing-assistant draft is what you would run through the detector to see how AI-like it scores; the paraphrasing-tool output generally scores lower (because it has been through a second AI rewrite that varies the structure) but still carries AI signatures. The grammar-checker is useful for cleaning up human writing that falsely scores as AI — awkward grammar sometimes reads as human where clean grammar reads as AI. The word-counter confirms you have enough text (over 100 words) for the detector to produce a meaningful score.
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