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How Does AI Detection Work? The Truth Behind AI Spotting Tools
Writing with AI
10 min read

How Does AI Detection Work? The Truth Behind AI Spotting Tools

AI detectors are being used everywhere. Learn how do they work, how reliable are they with our detailed guide.
Written by
Catherine B.
Published on
Mar 7, 2025
Table of contents
In 2023, Turnitin flagged over 2.1 million student papers for AI use within months of launching its detector. Universities rushed to update their academic policies, and a lot of students got stuck trying to prove they were actually human.
Like at Texas A&M where one prof made headlines for failing an entire class because an AI detector said their essays were written by ChatGPT. Except… they weren’t. The tool got it wrong. The students wrote their work, but the professor trusted an AI detector that wasn’t as accurate as he thought.
This isn’t just happening in schools. Writers and businesses are being asked to prove their work is human-made. And while AI writing tools are getting better and harder to catch, AI detectors are in a constant race to stay ahead.
If you’re using AI in your writing, you’re not alone. Tools like StudyPro help with that, but knowing how AI detection works can save you from headaches later.
So, what are they looking for? And can they really tell AI writing from human writing? Let’s see.

What Is an AI Detector? Basics to Understand

AI detectors (aka AI writing or AI content detectors) are like lie detectors for text: they try to figure out if something was partially or fully written by an AI tool like Claude or a human. But it’s not all that simple.
How do AI detectors work? Here are a few basics:
→ They scan the text for patterns: sentence structure, word choice, predictability, and more. If it looks too smooth, too repetitive, or too robotic, they flag it.
→ Schools use them to check if students wrote their essays. Businesses use them to catch fake reviews. Publishers use them to filter out AI-generated articles.
→ But they’re not perfect. AI detectors don’t understand writing like humans do. Instead, they look for mathematical patterns, which means they sometimes flag human writing as AI or let AI-generated text slip through.
Right now, AI detectors are still a work in progress. Some tools are more accurate than others, but no AI detector is 100% reliable (good news!). That’s why knowing how they work (and where they fail) matters.

What Are AI Detectors Used For?

AI detectors are being used everywhere, from job applications to newsrooms to social media. Some people trust them, some don’t, but one thing’s clear: AI-generated content is getting harder to spot, and these tools are the first line of defense.
Who Uses AI Detectors?
Why They Use Them
Professors & Universities
To check if students write their essays. Some schools now have strict AI policies, and getting flagged can mean failing an assignment, even if the detector is wrong.
Newsrooms & Publishers
To avoid AI-written articles slipping into journalism. Readers expect facts, not AI-generated filler, and some platforms have already been caught publishing AI content without realizing it.
Hiring Managers & Recruiters
To screen job applications. If AI wrote a cover letter, does the candidate have the needed skills? Some companies now scan applications for AI-generated text before even reading them.
Social Media Platforms
To catch AI-generated fake news and bot accounts. AI-written posts are flooding platforms, and some are nearly indistinguishable from human content.
Freelancers & Writers
To make sure their work isn’t flagged as AI before submitting it. Some clients won’t accept content if it gets marked as AI-generated, even if it’s written by a real person.

How Reliable Are AI Detectors?

Okay, so premium AI detectors like Originality.ai claim up to 99% accuracy, with less than a 2% false positive rate, so they supposedly rarely mistake human writing for AI. Sounds great, but this is under controlled testing.
Here are a few more interesting facts:
✔️ Turnitin’s AI checker, used by universities, still misses around 15% of AI-generated content, so some students using AI slip through.
✔️ A 2024 study testing 14 different AI content detectors found that most have an accuracy of under 80%, proving there’s no universal tool.
✔️ Human writing gets flagged as AI, with multiple student stories shared online of how their original essays written years ago before any AI involvement were marked as 100% AI-generated.
✔️ AI writing goes undetected. If an AI-generated essay is paraphrased or tweaked slightly, it can often bypass detection. Some AI tools, like an AI paraphrasing tool, are built specifically to reword text so it doesn’t trigger AI detectors.

How Does AI Detection Work

AI detection tools are always on the lookout for the small clues that reveal synthetic text. They sift through paragraphs, scanning for uniform sentence structures or suspiciously consistent language, and flag writing that shows these “robotic” signals. That can be stressful when you’ve spent hours on an essay, only to see it labeled as AI.
How do AI checkers work? At the core of this process, two technologies do the heavy lifting:
☑️ Machine Learning (ML): The brains behind AI detection. ML models are trained on massive datasets of human and AI-generated text, learning the subtle (and not-so-subtle) differences.
☑️ Natural Language Processing (NLP): The part that makes AI “understand” language. It helps break text down into tiny pieces – syntax, grammar, word frequency – to find telltale signs of AI writing.
These two technologies work together to spot patterns humans might miss. But AI writing is getting better. What sounded robotic last year now sounds human. AI detectors are constantly playing catch-up to stay ahead of AI tools that are getting more unpredictable by the day.
Let's briefly touch on how these technologies support AI detection tools.

Machine Learning (ML)

AI detection wouldn’t work without ML that helps AI detectors spot patterns in text, compare them to massive datasets, and decide whether something was written by a human or AI.
Here’s how it works in more detail:
→ Learning from data: AI detectors are trained on thousands (sometimes millions) of examples of both human-written and AI-generated text. They analyze the way people naturally write: our mix of short and long sentences and occasional mistakes. In contrast, AI writing is often too smooth and lacks variation.
→ Pattern recognition: Once the detector learns what human writing looks like, it scans new text for patterns that feel too artificial. If it sees repetitive sentence structures or an unusually low amount of “surprise” words, it raises a flag.
→ Anomaly detection: If a piece of writing feels too structured or has an abnormally high level of fluency (because AI doesn’t make typos or awkward phrasing errors like humans), the detector may classify it as AI-generated.
For example, let’s say an AI detection tool was trained on ChatGPT-generated essays. If a new essay follows the same predictable structure and avoids complex sentence variations, the tool recognizes those patterns and labels it as AI-written. But if AI tools like StudyPro’s AI Writer or ChatGPT get better at mimicking human unpredictability, detectors have a harder time keeping up.

Natural Language Processing (NLP)

If ML is the brain of AI detection, thenNLP is the part that understands language or, at least, tries to.
Here’s what NLP does in AI detection:
→ Splitting text into tiny pieces: The first step is tokenization, where the detector breaks a sentence into words or even smaller parts. This helps AI look at word frequency and structure. AI-generated text is often too clean and predictable, something NLP picks up on.
→ Checking sentence structure: AI tends to follow rigid grammar rules, while human writing is naturally messy, mixing short and long sentences and making the occasional mistake. NLP helps detectors spot when a text feels too uniform to be human.
→ Understanding meaning and flow: NLP analyzes the way words connect. Human writing has nuance and varied phrasing. AI writing, through, is often repetitive and a little too balanced.
Ever seen a fake-sounding five-star product review that says: “This is the best purchase ever! Great quality! Highly recommend!” That’s NLP catching repetitive, generic AI-generated reviews that don’t sound like a real person’s experience.

Key Techniques

So, how does AI detection work beyond just scanning for obvious patterns? AI detectors check how predictable the words are and whether the writing follows AI-like patterns. Some of the biggest techniques used include classifiers, embeddings, perplexity, and burstiness – fancy terms for methods that help detectors spot what sounds off.
Not sure what all these AI terms mean? Here’s a solid breakdown of AI terms that’ll help make sense of it all.

1. Classifiers

Imagine you're sorting emails into "spam" and "not spam" folders. That's essentially what classifiers do in AI detection.
They categorize text as either "AI-written" or "human-written:"
→ They get trained on tons of text: AI detectors don't guess. They study massive datasets of both human and AI-written content. This helps them learn the differences, like how AI tends to be too predictable or overly polished.
→ They look for patterns: Classifiers scan for things like sentence length and repetition. AI-generated writing often follows formulas and lacks randomness – things human writing naturally has.
→ They create rules to sort new text. Once a classifier understands the difference, it sets up a boundary between "AI" and "human." Every new text gets measured against this rulebook. If it leans too much toward AI patterns, it gets flagged.
For example, AI-written essays often use the same sentence structure over and over, making them feel robotic. A classifier would catch that repetitive rhythm and mark it as AI-generated.

2. Embeddings

When trying to explain a complex idea to someone who doesn't speak your language, you'd need a translator to convert your words into something they understand.
In the world of AI detection, embeddings act like that translator, converting words and phrases into numerical data that machines can grasp:
→ Words get turned into data: AI can’t understand words the way we do. Instead, embeddings map words as numerical values, so the detector can analyze how similar or different they are in meaning.
→ Context is everything: The word “bank” can mean a financial institution or the side of a river. Embeddings help AI figure out the right meaning based on the sentence.
→ AI detectors look for weird patterns: Human writing has natural variation. We mix things up and break rules. AI writing, though, is often too structured, and embeddings help AI detectors catch that.
AI might write something like: “This meal was a gastronomic delight.” It sounds fancy, but most people would just say: “This food was amazing.”

3. Perplexity

Perplexity is how AI detectors measure predictability in writing. AI-generated text tends to play it safe, choosing words that are statistically most likely to follow. If a sentence feels too predictable, it has low perplexity.
If a sentence is less predictable, it has high perplexity. Human writing is naturally more creative and surprising.
Let’s say you start a sentence with: “She took a deep breath and stepped into the…”
  • Low perplexity (AI-like): “room” or “building” → The safest, most predictable next words.
  • Medium perplexity (somewhat human-like): “storm” or “spotlight” → Makes sense but still is a bit unusual.
  • High perplexity (very human-like): “lion’s den” or “void of silence” → Unexpected and harder for AI to come up with.

4. Burstiness

Burstiness is the mix of short, long, and unpredictable sentences that make human writing feel dynamic. We don’t write in perfect, even patterns. Sometimes we keep it brief. Other times, we ramble, throwing in details and unexpected twists before getting to the point. AI often sticks to a repetitive structure, making its writing feel, well, flat.
Humans write with rhythm; AI writes with symmetry. When you tell a story, you might start with a long, winding thought and then suddenly (bam!) drop a quick, sharp sentence. AI-generated content tends to keep everything too uniform, with sentences that are all roughly the same length.
→ High burstiness: “She checked the clock. Five minutes. The bus was late again, as always. She shifted her weight from foot to foot, counting the cracks in the sidewalk, wondering if today was the day she’d finally cave and order a taxi instead.”
→ Low burstiness: “She waited for the bus. It was late. She looked around. The sidewalk had cracks. She thought about getting a taxi.”

AI Detectors vs. Plagiarism Checkers: What’s the Difference?

AI detectors and plagiarism checkers might sound similar, but they solve completely different problems. One asks, "Did AI write this?" while the other asks, "Was this copied from somewhere?"
Plagiarism checkers scan billions of sources (books, academic papers, websites) to find direct matches or paraphrased content. AI detectors don’t compare text to anything. Instead, they look for patterns and predictability to guess if a machine wrote it.
Here’s how they stack up:
Feature
AI Detectors
Plagiarism Checkers
Purpose
Figure out if text was written by AI or a human.
Spot copied or unoriginal content.
How It Works
Analyzes writing style, sentence flow, word predictability
Compares text to massive databases of books, academic papers, and web content.
What It Catches
Text that’s too structured, too repetitive, too “perfect”.
Copied, paraphrased, or uncredited material.
What It Misses
Won’t catch AI-written text if it’s well-edited or mimics human unpredictability.
Can’t detect AI-generated content unless it was copied from somewhere.
Who Uses It?
Professors checking student essays, recruiters screening resumes, publishers verifying content authenticity.
Teachers, editors, researchers making sure work is original.
Biggest Weakness
False positives. Sometimes flags human writing as AI just because it's structured too well.
Blind to AI. An AI-generated paper with no plagiarism won’t trigger any alerts.
Want to learn more about plagiarism and how it’s different from AI detection? Check out this guide on what is plagiarism to avoid common mistakes.

Detecting AI Writing Manually

AI detectors are useful, but sometimes you don’t even need one. If you know what to look for, you can often spot AI-generated text on your own.
Here’s how you can detect AI in writing without a tool:
☑️ Repetitive phrasing: AI loves repeating ideas in slightly different words. A sentence might say, "Exercise is important. Physical activity plays a crucial role. Staying active benefits health." Sounds fine at first, but read it again. It’s just rewording the same idea without adding anything new.
☑️ Overly formal and polite: AI tends to sound way too polite. Expect phrases like “It is important to note that…” or “One must consider…” instead of something natural like “Here’s the thing…” or “Let’s be real…”.
☑️ Lack of real-world experience: AI can write about things, but it can’t share personal experiences. If an article sounds detached and emotionless, there’s a good chance AI wrote it.
☑️ Hedging language. AI doesn’t like taking risks. It constantly plays it safe with phrases like “Some might say…” or “It is widely believed that…” without ever committing to a clear stance.
☑️ No sources or fake citations. AI isn’t great at fact-checking. It often forgets to cite sources or, worse, hallucinates and makes them up entirely. If you spot fake links or missing citations, you’ll know it’s AI.
☑️ Logical gaps or contradictions: AI writing sounds confident, but it sometimes contradicts itself or goes off track. A paragraph might start discussing climate change and suddenly switch to talking about electric cars without a proper connection.
The best way to get better at spotting AI? Use AI writing tools yourself. Run experiments and compare it to real human writing. Over time, you’ll start noticing patterns that make AI-generated content stand out.

Summing Up

How do AI detectors work? They break text apart and decide if it was written by a person or a machine. They check perplexity (is this too predictable?), burstiness (does this have natural sentence variation?), and other patterns only machines tend to follow. But AI is evolving fast, learning to fake unpredictability and mimic human randomness.
The AI detection game is a constant arms race. Writers tweak AI text to dodge detection. AI tools adjust to sound more human. Detection software updates to keep up. Repeat.
At the end of the day, AI detectors are just a tool. And like any tool, they’re only as useful as the person using them.

Frequently asked questions

Most colleges use Turnitin’s AI detector since it’s built into their plagiarism tool. Some professors also use GPTZero, which is designed for spotting AI-written essays. But these tools aren’t always right. Students have had real, human-written work flagged as AI, and some AI-generated papers have slipped through undetected. That’s why many schools don’t rely on AI detectors.
AI detectors check if writing is too predictable (low perplexity) or too uniform (low burstiness). The tech behind it is machine learning models trained on tons of text, learning the difference between human and AI writing.
Sources:
Fowler, G. A. (2023, April 1). ChatGPT is a cheat, and Turnitin is the detective. Here’s how they’re handling it. The Washington Post. https://www.washingtonpost.com/technology/2023/04/01/chatgpt-cheating-detection-turnitin/
Ede-Osifo, U. (2023, April 1). ChatGPT prompts Texas college instructor backlash. NBC News. https://www.nbcnews.com/tech/chatgpt-texas-college-instructor-backlash-rcna84888
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