How AI Content Detectors Work To Spot AI

By 2026, it’s estimated that a whopping 90% of online content could be AI-generated. It’s never been more important to be able to differentiate between human-generated and AI-generated content – but how do AI content detectors actually work? Here, we uncover the science behind these sophisticated systems.

What are AI content detectors?

AI content detectors are tools that analyze text to figure out if it was written by a human or an AI system. The tools use algorithms to look at how words are used, how sentences are put together, and what the text actually means – and compare the text to large collections of known AI-generated and human-written content. This helps them spot telltale signs of AI.

Why do AI detectors matter

As AI tools become more user-friendly and more of the world starts to engage with them, there has been a growing flood of AI-generated content, prompting concern over content integrity and quality. AI content detectors have become more popular recently across industries for several key reasons around maintaining standards around authenticity and trust: 

• Education institutions: Schools, colleges and universities use AI detectors to uphold academic integrity. They help educators find out whether students have used certain AI tools too heavily, which makes sure that submitted work is an honest reflection of a student’s understanding of the subject area.
• Businesses: In professional settings, AI tools are being used to create content at scale for websites, blogs and social media – as well as for general marketing materials and internal communications. AI content detectors help to make sure companies keep their brand voice and identity, as well as remaining an original and genuine reflection of the company.  
• Politics and journalism: With the rise of deepfakes and the spread of misinformation, AI detectors are used to grade the integrity of news and make sure false content is not mistaken for genuine information. This is particularly crucial during election cycles, when content can influence public opinion and threaten the democratic process. 

How does AI content detection work?

Many AI content detectors rely on the same techniques AI models like ChatGPT use to create language, including machine learning (ML) and natural language processing (NLP). 

Machine learning (ML)

Machine learning is about recognizing patterns – the more text is analyzed, the more easily the tools can pick up the subtle differences between AI-generated and human-generated content. Machine learning drives predictive analysis, which is critical for measuring perplexity (which we’ll get to later) – a key indicator of AI-generated text. 

Natural language processing (NLP)

Natural language processing is about the nuances of language, and helps AI detectors gauge the context and syntax of the text it is analyzing. AI can create grammatically correct sentences but tends to struggle with being creative, subtle, and adding depth of meaning (which humans naturally use in their writing). 

Classifiers and Embeddings

Within these broad categories of ML and NLP, classifiers and embeddings play important roles in the detection process. Classifiers place text into groups depending on the patterns they have learned: much like teaching someone to sort fruits based on characteristics they’ve learned, but applied to language. 

Embeddings represent words or phrases as vectors, which create a ‘map’ of language – this allows AI detectors to analyze semantic coherence. 

Key techniques in AI content detection

GPTZero was one of the first AI detectors to pioneer the idea of using "perplexity" and "burstiness" to evaluate writing. Since then, we have evolved our model beyond just these two factors, into a multilayered system with seven components to determine if text was written by AI – but it’s worth looking at where the model began. 

Perplexity

Perplexity is like a surprise meter when it comes to AI content detectors. The higher the perplexity, the more ‘surprised’ the detector is by the text it is seeing – as unexpected or unusual words or sentence structures tend to raise the perplexity score. If the text is ranked to have higher perplexity, it is more likely to be created by a human. If the text is too predictable, it’s likely to be AI authorship. 

Burstiness

Burstiness is a measure of how much the perplexity varies over the entire document – and is more about how the text flows. While human writing has a rhythm of short and long phrases, mixing up both simple and complex sentences, AI can often be a lot more predictable. Which means its sentences tend to be fairly uniform. 

This means AI generators often veer towards lower burstiness and create more monotonous text – repeating certain words or phrases too frequently because they've seen them appear often in their training data.

The interaction between perplexity and burstiness  

While perplexity is about the individual surprises of specific words and phrases, burstiness is more about the overall rhythm of a piece. 

A text with high burstiness can lead to higher perplexity as this is like a curveball to AI, with sudden shifts in sentence length making it harder for the AI to predict what comes next. But low burstiness often means lower perplexity – uniform sentences mean that AI has an easier time spotting the pattern and predicting the next words. 

AI content detectors look for a balance of perplexity and burstiness that mimic the way humans naturally write and speak. Too much of either perplexity or burstiness can be a red flag.  

Building a custom AI model to detect AI

As AI evolves rapidly to learn how to sound more human, AI detectors like GPTZero are also evolving to keep up with the new models. We’ve built a custom model that trains on human and AI text from the latest models to constantly evaluate for key differences between AI and human text beyond just perplexity and burstiness.

These days, our model also includes features like our Advanced scan, a sentence-by-sentence classification model; Internet Text Search, which checks if text has been found to exist in text and internet archives; and a shield that defends against other tools looking to exploit AI detectors. We combine these methods with even more dynamic ways to keep up with AI and the latest attempts to bypass AI detection.

How effective are AI detectors?

No tool can legitimately claim to be 100% accurate. Instead, the goal for any good AI tool should be to have the highest accuracy rate with the lowest rate of false positives and false negatives. GPTZero has a 99% accuracy rate and 1% false positive rate when detecting AI versus human samples. 

The real challenge is in detecting mixed documents that contain both AI-generated and human-written content. GPTZero is much better than our competitors at detecting mixed documents, with a 96.5% accuracy rate. 

GPTZero is trained on millions of documents spanning various domains of writing including creating writing, scientific writing, blogs, news articles, and more. We test our models on a never-before-seen set of human and AI articles from a section of our large-scale dataset, in addition to a smaller set of challenging articles that are outside its training distribution.

AI detectors vs. plagiarism checkers

While both AI detectors and plagiarism detection tools exist to verify the authenticity of content, they operate differently. AI detectors look at the text’s structure and word choice and overall style to see whether it was created by artificial intelligence or a human – and involves advanced algorithms and linguistic analysis. 

Meanwhile, plagiarism checkers are more straightforward and are essentially looking to match the text – and compare the writing against a broad data set of existing writing. When they spot similarities or matches, they will flag this as potential plagiarism. 

AI detectors exist to make sure the content is genuinely written by a human, while plagiarism checkers exist to confirm it is not copied from existing sources. However, neither type of tool is perfect – and it is important to review results critically and use them as input sources rather than singular and definitive judges. 

Limitations to watch out for

False positives or negatives

AI detectors work on probabilities, not absolutes – and can sometimes produce false positives or false negatives. This is because the systems are relying on algorithms which analyze patterns, and work by judging the likelihood of any given piece of content being produced by AI. Sometimes, human-written content can be mistakenly flagged as AI-generated. And similarly, AI-generated content can be mistakenly flagged as human-written. At GPTZero, we dive more into how our benchmarking works and how we arrive at our accuracy rates.

Trained on English language

Most AI detectors are trained on English language content, and can recognize structures and patterns that are commonly found in English. An AI detector might be less (or not at all) effective when analyzing multilingual content, or text in other languages. For non-English content, an AI detector might not be able to recognize specific characteristics it has been trained on, because it differs from what it is trained to recognize – making it less reliable. 

Here at GPTZero, we’ve made a lot of progress in addressing language-based limitations by using a more rigorous evaluation dataset. Through refining model training and bringing in de-biasing techniques, we’ve improved GPTZero’s reliability across different language contexts.   

Writing aids that increasingly use AI 

Many AI detectors usually cannot tell the difference between ethically used AI assistance (with grammar tools such as Grammarly) and completely AI-generated content. Some systems cannot always differentiate between someone using AI for minor edits like grammar corrections, or leaning on AI to generate the entire text. GPTZero has trained our models to account for this.   

How to use AI detectors responsibly

With all of the above in mind, AI content detectors should be treated as tools in a broader strategy when it comes to gauging content integrity – as opposed to standalone judges of content quality. To use AI detectors effectively, we need to recognize their limitations and bring in human judgment for the specific context in which the content is being created. 

The bottom line is that these tools are not ultimate authorities. They should complement human judgment, especially in situations where false positives or negatives can have long-reaching consequences. The central way to use AI detectors responsibly is to take a balanced approach that prioritizes fairness above convenience.