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 patterns other readers and educators have catalogued closely, even though most of that cataloguing is informed observation, not peer-reviewed research. Here are seven of the most common ones, and an important caveat that applies to every single one of them.

1. Transition-word stacking

Observers of AI-authored material have long noted a proclivity for repeating a narrow group of formal connectors at the beginning of back-to-back sentences. Phrases such as "Moreover," "Furthermore," "Additionally," and "Consequently" appear in close succession far more often than in texts crafted by people. Blogger and educator round-ups of AI-writing tell-tales list this habit as a leading indicator (Wandering Educators) — informal, practitioner-compiled observation rather than a peer-reviewed study, but one that lines up with what most frequent readers of AI text notice too. Human writers tend to apply these links sparingly, rarely stacking three 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." Educator-compiled lists of AI-writing tells flag excessive hedging as a hallmark pattern (The Augmented Educator) — again, practitioner observation rather than a controlled study. A little hedging is normal, honest writing. A paragraph that hedges every single claim usually isn't.

3. Suspiciously even sentence rhythm

When people write, they alternate compact, striking sentences with longer, more elaborate ones. In generated text the variation collapses, producing sentences of similar length throughout many paragraphs. The reading experience is consistently fluid but devoid of the jagged rhythm that reflects human thinking. Researchers call this pattern the "burstiness" signal.

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. One case study applying a bespoke linguistic framework to a single government policy report found this same genericness pattern in that document (researchleap.com) — worth reading as one detailed example, not a broad, measured finding across AI writing generally; it's a single case study on one text, not a large-scale study of the pattern, and the venue isn't a mainstream, widely-indexed academic journal.

5. The five-paragraph symmetry

Prompting a language model for an essay often produces a picture-perfect skeleton: intro, three parallel body parts, conclusion that circles back to the start. Real writing is rarely that uniform. People let their thoughts dictate length — one point expands into several paragraphs, another wraps up in a sentence, because structure follows what there is to say rather than a fixed template.

6. Overuse of rule-of-three lists

Artificial intelligence usually structures explanations in sets of three: three adjectives, three examples, three reasons, packaged in matching 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 naturally vary and 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. 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) — a crowd-sourced observation worth taking seriously as a shared reader instinct, even without a formal study attached to it.

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, for most people, most of the time: human eyeballing alone isn't reliable. The UK's National Centre for AI found that humans are generally worse than AI detection software at identifying AI-generated writing. They also flag more false positives when they try to call it by feel (JISC National Centre for AI, 2025). Here's the important exception. The research JISC draws on found that people who frequently use ChatGPT for writing are actually accurate, robust readers of AI-generated text — closer to a trained detector's performance than to the average casual reader's guesswork (Russell et al., arXiv:2501.15654). So spotting these seven patterns is a genuinely useful first instinct, if you've built that kind of practiced eye. It's just not a verdict. That holds even for dedicated detection tools: detectors themselves can misfire on genuine human writing, which is exactly why any single score, human or machine, works best as a signal rather than a ruling.

Turning instinct into an actual answer

If a text triggers two or three of these signals, take it seriously — but the only way to shift from "this feels off" to an actual statistical answer is to run it through a detection engine that scores perplexity, burstiness, and model-specific signatures across the entire document, not just a handful of surface patterns.

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