Detectors Guide
AI Detection Tools Explained: Turnitin, GPTZero, Copyleaks, and What They Really Look For
Learn how AI detectors actually work, in plain language: what "perplexity" and "burstiness" mean, how tools like Turnitin and GPTZero use them, and why scores are estimates—not proof.
AI writing vs AI detection
Writing models and detection models both study patterns in language—but for opposite goals.
Perplexity & burstiness
Two simple ideas—predictability and variation—sit under most "AI probability" scores.
Scores, not verdicts
Detectors estimate, not prove, whether text is AI-written, which is why human review still matters.
Artificial intelligence writing tools like ChatGPT have transformed the way we write essays, reports, and social posts. But with this convenience came a big question for teachers, editors, and institutions: how can you tell if something was written by a human or AI?
That’s where AI detection tools like Turnitin, GPTZero, and Copyleaks come in. In this article, we’ll break down how AI detection tools work—not through complex math, but by understanding the key signals they look for: perplexity and burstiness.
What Are AI Detection Tools?
AI detection tools analyze text and estimate the likelihood that it was generated by artificial intelligence. They don’t literally “catch” ChatGPT, per se; instead, they look for writing patterns that are too consistent, too smooth, or too predictable—traits common in AI writing.
Some of the main tools include:
- Turnitin: Integrated into many universities, it combines plagiarism checking with AI writing detection.
- GPTZero: Created by a student, it focuses on revealing automated writing by analyzing perplexity and burstiness scores.
- Copyleaks: A popular tool for organizations and publishers, known for highlighting AI-generated text portions within a document.
They all share a similar goal: flag text that behaves more like it came from a language model than from a person.
The Two Core Signals: Perplexity and Burstiness
Understanding how AI detection tools work starts with two surprisingly simple ideas about language and rhythm.
Perplexity: Measuring Predictability
Think of perplexity as a “surprise meter.” When humans write, our word choices are full of small surprises—we use idioms, change tone, or vary sentence flow. AI models, on the other hand, often pick the statistically most likely next word.
Low perplexity = very predictable = likely AI-generated.
High perplexity = less predictable = sounds more human.
For example, an AI might write:
“The weather today is sunny and bright.”
Simple, grammatically perfect, and unsurprising. A human might say:
“It’s one of those sunny days that almost makes you forget it’s a weekday.”
More spontaneous—and higher perplexity.
Burstiness: Variation in Sentence Patterns
Burstiness looks at the variation between sentences. People tend to mix short, punchy sentences with long, complex ones. AI models often produce text with steady pacing—rhythmically even but lifeless.
Low burstiness = uniform sentence structure = possible AI.
High burstiness = varied sentence length = human touch.
It’s similar to how a conversation feels different from a script. Real people weave pauses, emphasis, and rhythm naturally—AI doesn’t always mimic that well.
How Tools Combine These Clues
No detector relies on a single signal. Tools like Turnitin or Copyleaks combine several layers of analysis:
- Statistical scoring (perplexity/burstiness analysis).
- Stylometric features (word variety, punctuation, emotional tone).
- AI model fingerprinting, comparing your text to known AI output patterns.
They then output a probability score (for example, “75% likely AI-generated”). However, these scores aren’t definitive proof—they’re educated guesses based on writing behavior.
Are AI Detectors Always Accurate?
Not exactly. AI writing detectors can produce false positives, flagging genuine human writing as AI, especially when the style is overly formal or polished. Similarly, false negatives occur when an AI’s text is edited enough to look human.
That’s why most experts treat these detectors as guideposts, not verdicts. Context, human review, and transparency still matter most.
The Bottom Line
AI detection tools don’t think like humans—they measure patterns, not intent. They look for predictability, rhythm uniformity, and statistical fingerprints that differ between machine and human writing.
So next time someone says “AI detectors can always tell,” you’ll know the real story: they analyze how the text flows, not who wrote it.