The last two years alone have seen an amazingly rapid advancement of artificial intelligence (AI), so it’s no wonder that the need for AI detectors has grown exponentially. After all, generative models like ChatGPT and Claude have only become increasingly more accurate and natural with their outputs—and even then, the first version of these chatbots still read well.
It’s no wonder that the tools have become ubiquitous; today, millions of users are leveraging these advanced chatbots for a number of tasks, from simple brainstorming of creative ideas and writing to helping with coding. You could even have a conversation with the chatbot when you don’t feel like talking to real people.
Students can also use these Chatbots to help with their homework—and, of course, they can also cheat by having these AI chatbots write entire assignments for them. That, along with the general desire to know whether text has been AI-written, is a leading reason AI detectors have become such a vital tool.
But how do they work? In this article, we’ll delve into the inner workings of how most of these tools function, including AI Detector.
Fighting Fire With Fire, So To Speak
As you might have very well guessed, virtually all AI detectors are built similarly to how the chatbots themselves are built. They use the power of machine learning and large language models (LLMs), and to a lesser extent, neural networks and deep learning.
We’ll go into a lot more detail shortly, but for a quick primer, it all starts with machine learning. Machine learning is built on the foundation of a neural network, which is the attempt at creating computation systems that resemble human brain functioning. Then, as neural networks advanced to become more complex, they began to be called “deep learning” as a way to separate simple AI systems from more complex ones.
When all of these concepts come together, they result in an LLM. That’s a quick and very dirty explanation, but let’s start with the most basic concept: machine learning.
How We’ve Created Machines That Learn
In computer science, machine learning is the effort to develop computers that learn similarly to how we humans learn, which has given rise to the modern wave of AI. Think about AI-generated images and how far they’ve come in the last three years alone.
When AI first began trying to create images, the results were—shall we say—rough. But quickly, the AI image generators learned to perfect the process, and now good examples of AI images (as well as videos, text, and even audio) can be indistinguishable from the real thing.
Machine learning is facilitated by neural networks. “Neural,” naturally, comes from brain neurons, of which there are millions. While neural networks also have millions of nodes, the nodes are far fewer than the number of neurons that occur in the human brain.
Each node in a neural network, in essence, “weighs” data to find correlations between bits of information, gradually getting better and better at recognizing what they’re being trained to learn—and eventually, they develop the ability to replicate (or even detect) that data themselves.
Image credit: IBM.
Typically, a neural network will have only a few layers, including the input and the output. In between those two layers are the hidden layers—the ones that do the brunt of the work. If a neural network has a lot of these hidden layers, they’re colloquially referred to as “deep learning” networks, which is primarily a way to explain that they’re more advanced than typical neural networks.
Today, a lot of advanced AI models out there, like ChatGPT and Claude, use deep learning—or, in other words, really advanced neural networks. For the purpose of text-based AI detectors, these deep learning networks are the basis of an LLM, although different types of AI tools utilize different models. And they can all be called generative AI since they can use their newfound knowledge to generate output based on their input, from images to video to text.
How Does an LLM Work?
Once you have all your ducks in a row—or in this case, nodes in a neural network—you can use this technology to create an LLM. As their name implies, these models are best used to create natural language.
First, however, they must be trained, which puts those neural networks to work “learning” more about natural language. Only, in the case of AI detectors, those models must also be trained on not-so-natural language—or the words written by machines.
This training process requires vast amounts of data—we’re talking millions of words, both of what we’ve written and what AI bots like ChatGPT and Claude have written, as well.
It wouldn’t be far off to think that these LLMs work by “reading” these words. But in reality, it’s more that they’re processing the information. To help make this process more efficient, many AI models “tokenize” this text, which means that each word, phrase, and sentence are split into tokens—which are much easier for an AI model to process.
From there, because of that fancy deep learning network, it’s able to better understand the connection between those words and phrases and create natural language all on its own.
Similarly, not only can an LLM create natural language, but by tweaking the model, it can also recognize when any input you give it isn’t natural language created by a living, breathing person. And thus, we enter the world of AI detectors.
How AI Detectors Leverage LLMs To Do Their Job
While the foundation of most AI detectors is in many ways strikingly similar to models like ChatGPT and Claude—and even Copilot and Gemini—instead of outputting a response to your prompt, an AI detector’s output is a measure of the likelihood that the input you entered (in this case, it’s typically a piece of text) was AI-generated.
To perform this function, AI detectors must be trained on not only AI-written content, but also content written by flesh and blood people—and not just a little bit. The more data you train AI detector models on, the better they can tell the difference between what’s artificial and what’s real.
Where AI chatbots are concerned, if you take a moment to consider the number of them out there, you’ll see how herculean this task is.
On a similar note, no two human writers are the same, either, so to properly determine whether something has been written by a person, AI detectors must be trained on a wide variety of non-AI-written text as well.
Keeping Up With AI Technology
Great AI detectors aren’t developed, trained, and then left to collect dust on a website somewhere—they’re constantly maintained and retrained as new models come out. After all, it seems like AI chatbots are updated with new versions of their models almost on a monthly basis. In the last year alone, we’ve seen Claude 3, Claude 3.5, and ChatGPT 4o.
Claude 3.5, specifically, is a great deal “smarter”—at least artificially speaking—than the previous 3.0 model, and is highly competitive with GPT 4o.
Image credit: Anthropic.
Since each new chatbot version changes the output responses, improving grammar, syntax—and simply the nature of its responses—it’s imperative that AI detectors also update their models so that they can account for these changes. Otherwise, they’ll have a harder time determining whether something has been created by AI.
But how exactly do AI detectors tell that something is AI-generated?
How AI Detectors Are Able To Detect Anything
Anyone who’s read enough AI-written content can usually tell when something has been written by a machine, or at least they suspect it. But how can AI detectors pull off this feat with the level of accuracy that the best ones are known for?
No matter how natural AI-written text has become, there are still some telltale signs that eagle-eyed readers and AI detectors both catch. Often, the sentence structure and word choice of AI text become repetitive after a few paragraphs. Humans also write in a comparatively less predictable fashion, whereas AI tends to write with a lot more predictability, giving these outputs more of a droning rhythm.
While chatbot models have improved these drawbacks over the last couple of years, there are still telltale signs that something is AI-generated. These signs are what AI detectors are trained to identify.
And though AI detectors have become increasingly accurate since they were first introduced, they’re still sometimes capable of giving false positives (or even negatives). So, keep that in mind before jumping to any conclusions about the authenticity of anything you’re scanning.
What AI Detectors Are Used For
This accuracy—and the need to be cognizant of potential false positives—is crucial since often, AI detectors are used to determine whether a student is guilty of academic dishonesty.
Cheating is nothing new, and students have been committing various forms of plagiarism for as long as there have been schools. AI has only provided would-be cheaters with a new avenue to do what they would have probably done a different way.
While these tools should never be the final say in a student’s guilt, they can be a powerful tool when weighing other forms of evidence. False positives can and occasionally will happen, notably for non-native English speakers, who tend to write far more predictably than native speakers.
However, for legitimate writers, students, or otherwise, AI detection tools can be a benefit when trying to figure out whether a piece of writing needs to be taken up a notch since—understandably—nobody really wants to write something that might be mistaken for an AI-generated piece of text. For that, AI detectors work great.
However, with as long as academic dishonesty has been around, developing methods of detecting when it might be occurring is nothing new—even if AI has helped to revolutionize the process of checking essays and papers.
A Quick History of AI Detectors
While machine learning, neural networks, and LLMs revolutionized AI detectors, these sorts of helpful tools existed before our current trend of AI advancements.
Plagiarism checkers like Turnitin have existed for over 20 years, and before AI, these sorts of tools functioned by comparing the submitted paper with large databases—the kind of databases (Turnitin, for instance, uses proprietary databases and open web content) that would later go on to train AI models. An algorithm would then simply check the paper against one of these databases and identify any areas where the two overlapped. This method is called text-matching or string-matching algorithms.
With enough overlap, the checker would then deem that the paper was plagiarized. This worked well enough and over the years, these tools became faster and more efficient, and the databases powering them grew larger. What once took minutes began to take only seconds. The Plagiarism checkers also naturally expanded, adding new features and services for their clientele.
AI Enters the Scene
Then, of course, AI came around and completely changed how these plagiarism checkers functioned. In many ways, modern AI detectors can’t fully be compared to the plagiarism checkers of old—the deep learning networks powering many of them are vastly different from the way that checkers text-matched in the past. However, many services, such as Turnitin, rather than being left in the dust, opted to instead adopt AI detectors of their own that work alongside their classic plagiarism checkers.
Early AI detectors, such as GPT-2’s Output Detector, arose out of a need to double-check whether text had been AI-manipulated. In many ways, these early tools were quite similar to the ones used today. However, modern tools are much more advanced and efficient.
Double-Check Your Writing With AI Detector
No matter the type of writing, from academic papers or essays to informal blogs or social media posts, AI Detector can check that it isn’t AI-generated. Writers can double-check that they’ve written a high-quality piece, and not something that could be mistaken for AI. And publishers and teachers can ensure that submissions show no signs of being AI-generated, either.
A check takes only seconds and the detailed report that results will tell you everything you need to know about the submitted writing.
Frequently Asked Questions
1. Can AI detectors guarantee 100% accuracy?
No. While AI detectors have improved significantly, they’re not flawless—although they’re creeping closer and closer to that coveted 100%, with many detectors able to attain 96–98% accuracy. However, false positives and false negatives still occur, particularly when dealing with highly creative writing or text written by non-native speakers.
2. What happens if an AI detection tool makes a mistake?
If you believe that your AI detector has mistakenly given you a false positive, it’s good to contact the team so that they can further improve their tool. Also, in educational or professional settings, it’s essential to treat AI detection results as part of a broader evaluation process. Mistakes happen, and tools should be used alongside human judgment to avoid unfair penalties for false positives.
3. Can AI detectors recognize all forms of AI-generated content?
Yes, for the most part. But it’s a constantly moving goalpost. As AI models evolve, especially with advancements that make their output more creative and natural, detectors must be constantly updated to keep pace. Typically, the brief period immediately after a new chatbot model is released will prove to be the most difficult for AI detectors until they themselves have been updated to catch up.
4. What are the key differences between AI detectors like Turnitin, GPTZero, AI Detector, and others?
Different detectors may focus on different metrics—some focus on patterns of predictability while others might use token-based analysis. Accuracy can vary depending on the model, the dataset it was trained on, and the nature of the input text. That’s why it’s important to choose a detector that’s trained on all of the models, especially the most recent ones.
5. How are AI detectors updated to handle new versions of AI like ChatGPT or Claude?
With each new version of a generative AI model released, AI detectors must be retrained and refined to detect the nuances that have changed with each subsequent change to the AI text generator models. This typically means that just after a new version of a chatbot releases, it might be a short while before AI detectors have caught up. Fortunately, AI detectors have gotten quite good at this process over the last year.
