About
Built to answer one question:
Did AI write this?
Original AI Checker is a free AI content detection tool built for educators, editors, and anyone who needs to know whether a piece of text was written by a human — or generated by a language model.
5+
AI models in detection set
250+
Minimum words for reliable results
0
Text stored after analysis
Free
No account required to check
Our Mission
Why we built this
Since the public release of ChatGPT, the volume of AI-generated content on the web — and in academic submissions — has grown dramatically. Teachers are asking whether their students wrote their own essays. Editors are asking whether submitted articles were written by a human. Employers are asking whether job applications are genuine.
We built Original AI Checker to give a fast, free, no-login answer to that question. Not a definitive verdict — AI detection is probabilistic, and we’re upfront about that — but a useful, honest signal based on real linguistic analysis.
What we’re not
We’re not a plagiarism checker. We don’t compare text against a database of known sources. We analyze the statistical and linguistic fingerprints that distinguish AI-generated text from human writing.
What we are
A probabilistic AI detection tool that analyzes perplexity, burstiness, and model-specific linguistic patterns to return a confidence score — and a full sentence-level breakdown in the detailed report.
Methodology
How detection works
AI detection relies on statistical patterns that differ between human and machine-generated text. Here’s what the engine analyzes:
01
Perplexity Analysis
Perplexity measures how “surprised” a language model is by a given text. AI-generated text tends to have low perplexity — the model produces predictable, high-probability word sequences. Human writing, even when grammatical, is more unpredictable.
02
Burstiness Scoring
Burstiness measures the variation in sentence length and complexity within a passage. Human writing has high burstiness — long and short sentences mix naturally. AI tends to produce uniformly structured output with consistent sentence lengths.
03
Model Signature Recognition
Different AI models leave different statistical fingerprints. The detection engine has been trained against reference outputs from GPT, Claude (Anthropic), Gemini (Google), Mistral, and Llama-family models to identify model-specific patterns.
04
Sentence-Level Flagging
Rather than scoring a document as a whole, the full analysis breaks down each sentence — identifying which segments are most likely AI-generated and which appear human-written. This is available in the detailed report.
Honest Limitations
What we don’t get right — and why
No AI detector is perfect. We believe in being transparent about where these tools fail so you can use them responsibly.
✍️
ESL Writers
Non-native English writers tend to produce grammatically uniform, low-burstiness text — the same pattern as AI. This leads to elevated false positives. Treat high scores for ESL writers with extra caution.
✂️
Heavily Edited AI Text
AI text that has been substantially rewritten by a human loses many of its statistical fingerprints. A heavily edited AI draft may score low. The tool reflects the final text as written, not its origin.
📏
Short Texts
Texts under 100 words don’t provide enough statistical signal for reliable results. Aim for 250+ words. A single paragraph will not produce a meaningful score.
🔄
New Models
As new AI models are released, detection needs to be updated. There may be a lag period during which very recent model outputs are harder to detect. We update the detection set regularly.
The Team
Who’s behind this
Original AI Checker is maintained by a small team of NLP researchers and developers with backgrounds in computational linguistics, educational technology, and content integrity.
MR
Marcus R.
Lead NLP Engineer
8 years in computational linguistics. Previously built text classification systems for a major educational publisher. Responsible for the core detection model.
SL
Sarah L.
Research Lead
PhD candidate in applied NLP. Her research focuses on statistical methods for distinguishing human and machine-generated text. Oversees model evaluation and benchmarking.
DP
David P.
Product & Engineering
Full-stack developer with a background in EdTech. Built the tool interface, API layer, and makes sure everything stays fast, private, and accessible.
Try it now — free
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