Sometimes teachers can tell, and sometimes they cannot — because AI detection is a probability estimate, not a verdict, and it produces both false negatives (AI text that slips through) and false positives (human text flagged as machine-made).
A teacher who knows your writing well may spot a sudden change in voice faster than any software can. And the software itself is unreliable enough that most institutions treat a detector score as a reason to start a conversation, not as proof.
How detectors score text
Most AI detectors work by measuring two statistical properties of your writing. The first is perplexity — roughly, how surprised a language model is by each word you chose. Predictable word choices produce low perplexity; unusual ones produce high perplexity.
The second is burstiness — how much sentence length and structure vary from one sentence to the next. Human writing tends to swing around: a long, winding sentence followed by a short punchy one. Language models, by default, produce text that sits in a comfortable middle range, and detectors are trained to notice that flatness.
That is why a piece of genuinely human writing can get flagged. If you write in a very even, formal register — long sentences, tidy transitions, no slang — you are mimicking the statistical profile detectors associate with machines. The same happens with highly formulaic writing, like a five-paragraph essay written to a strict template.
A worked example
Picture two students answering the same prompt about whether social media harms teenagers.
Student A writes: "Social media affects teenagers in many ways. It can influence their sleep, their mood, and their friendships. Many experts believe it has both benefits and drawbacks. This essay will explore both sides." Every sentence is roughly the same length and the vocabulary is generic. A detector is likely to score this as machine-generated, even though Student A wrote it alone at a kitchen table.
Student B writes: "My cousin stopped sleeping. Not dramatically — she just started scrolling at 11pm and stopped noticing the clock. By spring she was averaging five hours a night, and her grades slid. Was that social media's fault? Partly. But her school also started at 7:15am, which is a separate problem." This has burstiness: a three-word sentence, a long one, a question, a concession. Same argument, different texture. Detectors usually score it as human.
The lesson is not "write weirdly to beat the machine." It is that the features detectors actually measure are style features, not truth features. They cannot tell you whether the ideas are original, whether the sources are real, or whether the student understands the topic.
When detectors are wrong
Detectors fail in both directions, and the failures are not evenly distributed. Non-native English speakers have historically been flagged at higher rates in some published studies, and so have writers who use heavy editing tools or translation software. If you write in a second language, or you draft in one language and polish in another, your text may carry the flattened statistical signature that detectors read as machine-made.
There is also a practical limit: detectors cannot see your process. They see a final document. If you used AI to brainstorm, outline, or check grammar, the output may still read as fully yours — or it may not, depending on how much the tool rewrote. The score reflects the text, not the workflow.
What to do if you are flagged
First, do not panic and do not delete anything. Keep your drafts, your notes, your browser history, and any version history from your writing app. The strongest defence against a false positive is evidence of process: an outline in your own handwriting, a messy first draft, a comment thread where you asked a friend to read it.
Second, talk to your teacher before it becomes a formal accusation. Explain what tools you used and for what. Most academic integrity policies distinguish between using AI to generate content and using it to check grammar or find sources — but the rules vary by institution, so read your syllabus rather than assuming.
Third, if you did use AI, say so. A detector score is weak evidence, but a caught lie is strong evidence. The conversation goes very differently when you admit the tool use and can explain your own reasoning about the topic.