Spotting AI-Generated Text
AI-generated text is everywhere now: product reviews, news-style articles, social posts, emails, even homework. Some of it is harmless. Some of it is designed to mislead, sell, or flood a topic with low-quality filler. Learning to read text with a trained eye helps you decide how much weight to give it, and, just as importantly, teaches you the limits of the AI "detectors" people often reach for.
The honest headline of this lesson: there is no reliable single test that proves a piece of writing was made by AI. What you can do is notice patterns, weigh them as soft signals, and lean on verification of the claims rather than guessing about the author.
What You'll Learn
- The common tells of AI-generated text, and why each is only a soft signal
- Why AI text detectors are unreliable and unfair to some writers
- How to shift from "who wrote this?" to "is this true?"
- A quick read-through routine for suspicious text
Common Tells (Treat Each as a Soft Signal)
None of these proves anything on its own. A skilled human can write this way, and a careful AI user can avoid all of them. Treat them as smoke, not fire: reasons to look closer, not verdicts.
- Fluent but empty. The text is grammatically perfect and confident yet says very little. It restates the question, hedges in every direction, and never commits to a specific, checkable claim.
- Generic structure. Tidy intro, three or four evenly weighted points, neat conclusion that begins with a phrase like "In conclusion." Real expertise tends to be lopsided, dwelling on what actually matters.
- No specifics or sourcing. Few names, dates, numbers, or first-hand details. When numbers do appear, they are round and uncited.
- Subtly wrong details. Plausible-sounding facts that are slightly off, a misattributed quote, an invented study, a date that does not line up. This overlaps with hallucination, which the fact-checking lesson covers in depth.
- Uniform tone. No quirks, no strong opinions, no lived experience. Every paragraph sounds the same temperature.
Remember the trap from the last lesson: AI removed the old tell of poor spelling and grammar. Clean writing is now a non-signal. Absence of errors tells you nothing.
Why AI Text Detectors Are Unreliable
When people want certainty, they paste text into an "AI detector" and trust the percentage it spits out. This is a mistake, for reasons worth understanding so you do not over-rely on these tools or, worse, accuse someone based on one.
- They produce false positives. Independent testing has repeatedly found detectors flagging genuinely human writing as AI, sometimes at meaningful rates. A high score is not proof.
- They are biased against some writers. Multiple studies have found that text from non-native English speakers is flagged as AI far more often than text from native speakers, because their phrasing can look "simpler" or more formulaic to the detector. Using detector scores to make decisions about people can be actively unfair.
- They are easy to evade. Light editing, paraphrasing tools, or a different model can drop a detector's confidence to near zero. The people most determined to deceive are the most able to slip past.
- Scores conflict. Run the same text through several detectors and you often get different answers. There is no agreed ground truth.
Detectors guess at the author. Verification checks the content. The second is what matters.
| Criteria | AI text detector score | Verifying the claims |
|---|---|---|
| What it tells you | A probability guess about authorship | Whether the content is actually true |
| False positives | Common, and unfair to some writers | Not applicable, you check facts directly |
| Can it be gamed? | Yes, with light editing | No, a false claim stays false |
| Good enough to accuse someone? | No | It is the right thing to focus on |
AI text detector score
- What it tells you
- A probability guess about authorship
- False positives
- Common, and unfair to some writers
- Can it be gamed?
- Yes, with light editing
- Good enough to accuse someone?
- No
Verifying the claims
- What it tells you
- Whether the content is actually true
- False positives
- Not applicable, you check facts directly
- Can it be gamed?
- No, a false claim stays false
- Good enough to accuse someone?
- It is the right thing to focus on
The practical takeaway: a detector can be one weak input among many, but never a verdict. Do not fail a student, reject a writer, or call something fake on a detector score alone.
Shift the Question: From "Who?" to "Is It True?"
Whether a paragraph was typed by a person or generated by a model usually matters less than whether it is accurate and trustworthy. A true, well-sourced fact is useful no matter who assembled it. A false claim is harmful whether a human or an AI produced it.
So when text matters, redirect your attention:
- Pull out the specific, checkable claims (a statistic, a quote, an event, a date).
- Verify those against independent, credible sources, which the next lessons teach in detail.
- Judge the content, and let the question of authorship fade into the background where it usually belongs.
A Quick Read-Through Routine
When a piece of text trips your triggers, run this in under a minute:
- Skim for substance. Strip away the fluent wrapper. What specific, falsifiable claims remain? If almost none, the text may be filler regardless of who wrote it.
- Spot-check one claim. Take the most surprising or load-bearing claim and search for it independently. If it does not hold up, distrust the rest.
- Check the source and motive. Who published this, and what do they gain if you believe it? An anonymous site selling a product is different from a named reporter at an established outlet.
- Resist the detector reflex. If you use one at all, treat the score as a faint hint, never a conclusion, and never as grounds to accuse a person.
Key Takeaways
- AI text tells (fluent but empty, generic structure, no specifics, uniform tone) are soft signals to look closer, never proof.
- Clean grammar is no longer a signal of a human author; absence of errors tells you nothing.
- AI text detectors are unreliable, produce false positives, are biased against non-native English writers, and are easy to evade, so never use a score to accuse someone.
- Redirect from "who wrote this?" to "is this true?" and verify the specific claims instead.