How to Check If Text Is AI Generated: A Complete Guide

Updated April 2026 · 8 min read

Checking whether a piece of text was written by AI has become a practical skill — for teachers reviewing student submissions, editors evaluating content, and writers auditing their own AI-assisted drafts. This guide walks through the process: what to look for by eye, what tools measure automatically, and how to interpret the results without overclaiming.

Start with a manual read

Before running any tool, read the text and notice your intuitive reaction. AI-generated text produces a specific kind of reading experience: smooth but slightly distant, correct but slightly generic. You follow it fine but don't feel pulled forward by it. That low-engagement feeling is a real signal — trust it as a starting point, not a conclusion.

Then look for specific tells.

Sentence rhythm

Read the text aloud or scan the sentence lengths visually. Human writers naturally produce wildly varied sentence lengths within a paragraph — short punches, long winding ones, the occasional fragment. AI text is metronomic: most sentences cluster in the same length range, paragraph after paragraph. If every sentence feels like it's the same duration, that's a flag.

Transition phrases

Scan for "furthermore," "moreover," "additionally," "it is important to note," "in conclusion," and similar connectors. One or two in a document is normal. A half-dozen in a single article is suspicious. AI uses these at roughly three times the rate of typical human writing, because they're statistically safe choices.

Vocabulary patterns

Notice repeated words within short spans. AI models have strong preferences — "significant," "crucial," "leverage," "comprehensive," "robust," "ensure" show up more often than they would in human writing. Repetition within a few paragraphs is a clearer signal than the words themselves.

Opinion and specificity

Does the text take positions, or does it hedge everything? "Many experts believe..." "There are various perspectives on..." "It could be argued that..." AI is trained for neutrality and tends to avoid strong claims. Human writing — even formal human writing — typically stakes out at least some clear positions and includes specific examples, names, or numbers that came from actual experience or research.

Use a detection tool

Manual reading is useful but imprecise. Free AI content detectors automate the process and give you measurable scores. Here's what good tools measure:

• Burstiness score — quantifies sentence length variance. A low score means uniform, AI-like rhythm.
• Lexical diversity — the ratio of unique words to total words. AI text consistently shows lower diversity.
• Repetition score — frequency of repeated phrases and connector overuse.
• Perplexity — how predictable the text is according to a language model. AI text is unusually predictable.

RealText measures the first three and shows you which specific patterns are driving the score. That's more actionable than a single probability number, especially if you're checking your own writing rather than judging someone else's.

How to read the results

A high AI-probability score doesn't prove the text was AI-generated. It proves the text has stylistic properties that cluster with AI writing. Those same properties appear in:

• Formal academic writing by non-native English speakers
• Technical documentation written to a corporate style guide
• Writers who naturally favor structured, formal prose

This is the false positive problem, and it's serious. No free detector is reliable enough to be used as sole evidence for an accusation. Treat scores as a signal that warrants closer reading, not a verdict.

A low AI-probability score means the text doesn't strongly match AI patterns. It doesn't confirm the text is human — it confirms the text doesn't look like typical AI output. Those are different claims.

Practical workflow

1. Read the text manually and note your initial impression.
2. Look for specific tells: rhythm, connectors, vocabulary repetition, hedging.
3. Run it through a detector and check individual metrics, not just the overall score.
4. Compare the metrics against your manual observations — do they align?
5. If patterns are ambiguous, consider context: does the author have a consistent writing history? Does the content include specific knowledge or experience that would be unusual for AI to generate?

When checking your own text

The workflow above applies when you're evaluating someone else's writing. If you're checking your own AI-assisted drafts before submitting or publishing, the goal is different: you want to find the parts that sound most AI-like and improve them. Run the detector, read the per-paragraph breakdown if available, and focus your editing on the weakest sections. Making AI text sound natural is a learnable skill, and the metrics give you a precise target.