7 Telltale Signs a Piece of Writing Was Generated by AI (and Why They're Not Enough on Their Own)

Readers have gotten sharper. Two years into the mainstream AI-writing era, a lot of people can now sense when something feels "off" about a piece of text before they can articulate why. That instinct isn't random — it's picking up on real, well-documented linguistic patterns. Here are seven of the most common ones, drawn from research on AI-generated text, and an important caveat that applies to every single one of them.

1. Transition-word stacking

AI writing has a well-known tic: opening consecutive sentences or paragraphs with the same handful of formal connectors — "Moreover," "Furthermore," "Additionally," "Consequently." Educators cataloguing AI-writing tells have flagged this specifically as one of the most common offenders (Wandering Educators). Humans use these words too, just far less relentlessly — and rarely three sentences in a row.

2. Excessive hedging

AI models are tuned to sound balanced and non-committal, which often produces a wall of qualifiers: "it's important to note," "this can vary," "in many cases." Research on AI-generated text explicitly identifies excessive reliance on hedging language as a hallmark pattern (The Augmented Educator). A little hedging is normal, honest writing. A paragraph that hedges every single claim usually isn't.

3. Suspiciously even sentence rhythm

This is the "burstiness" signal in plain language: human writing naturally alternates short, punchy sentences with longer, winding ones. AI-generated passages tend to settle into a narrower band of sentence length, paragraph after paragraph. It reads smoothly, but it reads evenly — missing the natural unevenness of someone thinking on the page.

4. Generic, low-specificity examples

When a model needs an example, it tends to reach for the safest, most statistically common one available — "imagine a small business owner trying to grow their customer base" instead of a specific, textured detail a real person with real experience would include. Academic work distinguishing linguistic and cognitive markers of AI-generated communication has found this genericness to be a consistent, measurable pattern rather than just a vibe (researchleap.com).

5. The five-paragraph symmetry

Ask an AI model for an essay and you'll often get a suspiciously tidy structure: intro, three neatly parallel body points, conclusion that restates the intro. Real writing is messier — points run long or short depending on how much there actually is to say about them, not because an outline says each one gets equal space.

6. Overuse of rule-of-three lists

AI-generated text leans hard on triads: three adjectives, three examples, three reasons, packaged in parallel grammatical structure. It's a genuinely effective rhetorical device — which is exactly why models default to it constantly, well past the point where a human writer would just say two things, or five.

7. A too-perfect emotional register

Genuine writing has emotional texture that doesn't always match the topic tidily — a little irritation bleeding into an explainer, a joke that doesn't quite land, a tangent about something unrelated. Formal academic analysis on spotting AI-generated text distinguishes between these "subtle" cognitive-texture signs and the more obvious surface tells, and notes the subtle ones are actually harder to fake deliberately (SSRN, 2025). AI writing, even when instructed to sound casual, tends to stay evenly polished — genuinely messy emotional register is hard to reproduce on purpose. Readers online have started noticing the same pattern colloquially, describing writing that's technically fluent but somehow emotionally flat as "AI slop" (r/OpenAI).

The catch: none of these prove anything on their own

Here's the honest caveat that has to come with a list like this: every one of these seven signs can also just be someone's personal writing style. Some people genuinely love transition words. Some non-native English speakers hedge more out of caution, not AI use. Technical writers often produce deliberately even, structured prose because clarity is the goal, not because a model wrote it.

This is exactly why research keeps landing on the same conclusion: human eyeballing alone is not reliable. The UK's National Centre for AI found that humans are generally worse than AI detection software at identifying AI-generated writing, and tend to have higher false-positive rates when they try to call it by feel (JISC National Centre for AI, 2025). Spotting these seven patterns is a genuinely useful first instinct — it's just not a verdict.

Turning instinct into an actual answer

If a piece of writing is tripping two or three of these signals for you, that's worth listening to — but the only way to move from "this feels off" to an actual statistical answer is to run it through a detection engine that's scoring perplexity, burstiness, and model-specific signatures across the full text, not just a handful of surface patterns.

Paste the passage into TheChecker.AI's free demo and see what the underlying statistics actually say. It takes seconds, and it turns a hunch into a number you can act on.