How to Check If Text Is AI Generated — A Practical Guide for 2026

AI-generated text is everywhere in 2026. The ability to check text reliably — and interpret those results correctly — has become a practical skill. Here's how to do it without making the mistakes that lead to wrong conclusions.

Running text through an AI detector takes seconds. Understanding what the result means, when to act on it, and what to do when the result is ambiguous — that takes more thought. This guide covers the full workflow: from pasting text to making a fair, defensible judgment about what you're looking at.

Why people check for AI-generated text

The use cases for AI detection are broader than most people realize. They fall into a few main categories:

• Educators reviewing student submissions — checking whether an essay was written by the student or generated by ChatGPT or Claude.
• Content publishers and editors — verifying that freelance or outsourced content is genuine writing rather than AI output submitted as original work.
• Writers checking their own text — understanding how heavily an AI-assisted draft scores so they know how much editing is needed before publication.
• HR and hiring professionals — checking whether cover letters, writing samples, or take-home assessments were AI-generated.
• Researchers and journalists — evaluating whether public statements, testimonials, or submitted content are authentic.

Each of these use cases has a different threshold for what counts as "AI-generated enough to matter" — which is one reason interpreting results correctly is more important than just running the check.

What detectors actually measure

AI detectors don't read text the way humans do. They measure statistical properties of the text that correlate with AI versus human authorship. The two main signals are perplexity and burstiness.

Perplexity measures how predictable the word choices are. Language models generate text by selecting statistically probable words — which means AI text tends to be low-perplexity (predictable). Human writing is less predictable because humans make unexpected word choices and take conversational detours. Low perplexity is a signal of AI authorship.

Burstiness measures variation in sentence length and structure. Humans write with natural rhythm variation — a long complex sentence followed by a short punchy one. AI defaults to consistent, moderate sentence lengths. Low burstiness is a signal of AI authorship.

More sophisticated detectors layer classification models trained on labeled datasets of human and AI text on top of these signals. The result is a probability score, not a certainty. Every detector produces probabilistic assessments, not binary verdicts.

Step-by-step: running an AI detection check

1. Paste enough text

AI detectors need sufficient text to produce reliable results. As a rule of thumb, 300 words is a minimum for useful results; 500+ words is better. Very short texts (1–2 sentences) produce unreliable scores because there isn't enough statistical signal. If you're checking a long document, you can break it into sections and check each one separately.

2. Note the overall score

The overall score represents the detector's confidence that the text is AI-generated, expressed as a percentage. 90% means the model is highly confident the text is AI-generated. 20% means the model is fairly confident the text is human-written. 50% means the model genuinely can't tell. See the score zones below for how to act on these ranges.

3. Read the sentence-level highlights

Most modern detectors highlight individual sentences that are most likely AI-generated. These highlights are more useful than the overall score for understanding what's actually happening in the text. A document can score 70% AI overall but have most of the AI signal concentrated in one or two paragraphs — information that changes how you interpret and respond to the result.

4. Look at the pattern, not just the score

Is the AI flagging distributed evenly, or concentrated in specific sections? Distributed flags suggest the whole document was AI-generated. Concentrated flags may suggest selective use of AI assistance in specific sections, or may indicate the writing style of a particular section (introductions and conclusions often score higher for AI because of their conventional structure).

Interpreting the score — the three confidence zones

The most important zone is the middle: 30–70%. In this range, the detector genuinely cannot produce a reliable verdict. Acting as if a 55% score is definitive evidence of AI generation — in either direction — is a reasoning error. The correct response to a score in this zone is to gather more evidence, not to reach a conclusion.

Sentence-level flags — what they actually mean

When a detector highlights specific sentences as high-probability AI, those sentences share statistical properties with AI-generated text. But it's important to understand what that actually means for each type of sentence:

Introductory and concluding sentences

Opening sentences ("In today's rapidly evolving world...") and closing sentences ("In conclusion, it is clear that...") are heavily flagged by every detector because their conventional structure closely matches AI output patterns. This is one of the most common sources of false positives on human writing — human writers also use conventional openers and closers, and those conventional forms overlap heavily with AI patterns.

Definition sentences

"X is defined as Y" or "According to [source], Z means..." — definitional sentences have predictable structure that flags easily. When you see multiple definition sentences flagged, consider whether the topic itself (a formal subject requiring many definitions) is driving the signal rather than AI authorship.

Transition sentences

"Furthermore," "In addition," "Moreover," "It is important to note that" — these transitions are almost universally flagged because they appear so frequently in AI text. They also appear in human writing, particularly in academic and formal register. A document with many flagged transition sentences may be a formal human writer, not an AI.

Technical or factual sentences

Sentences that state facts, statistics, or technical information in precise language tend to score high for AI probability because precision and predictability are correlated. A medical or legal document written by a human professional may score higher than casual human writing because precision is a professional requirement, not an AI signal.

When detection results are wrong

Every detector produces false positives (flagging human writing as AI) and false negatives (missing AI writing). Understanding when these errors are most likely helps you contextualize results:

Higher false positive risk

• Formal academic writing — particularly in law, medicine, and sciences
• Non-native English writers who use conventional formal structures
• Writers who learned to write through highly structured curricula
• Technical documentation and instructional content
• Very short texts (under 300 words)

Higher false negative risk

• AI text that was edited by a human before submission
• AI text processed through a humanizer tool
• Very new AI models whose output patterns haven't been added to detector training data yet
• AI text written in response to prompts that instruct casual or varied style

What to do when your own writing gets flagged

If you're a human writer and your text scores high on an AI detector, you have several options:

Understand why it was flagged

Look at the sentence-level highlights. If the flagged sentences are mostly your introductions, transitions, and conclusions — those are the easiest fixes. Vary your sentence length more aggressively. Replace conventional transitions ("Furthermore") with specific connectives ("This pattern also appears in..."). Use contractions where appropriate. Start sentences with different structures.

Run it through a humanizer to understand the patterns

Running your own writing through an AI humanizer tool can help you see which specific patterns are driving the AI signal. You don't have to use the humanized output — the exercise of seeing which sentences get rewritten tells you a lot about what the detector is flagging. Then rewrite those sentences in your own words.

Document your writing process

If you expect your writing to be evaluated by an institution that uses AI detection, document your writing process as you go — drafts, outlines, notes, timestamps. This evidence of process is more compelling to a fair evaluator than any detector score.

Using multiple detectors for a second opinion

No single detector should be treated as definitive. Different detectors use different models, training data, and weighting schemes — which means they sometimes disagree significantly on the same text. Using two or three detectors and comparing results gives a more reliable picture:

• Both score 80%+: Strong, convergent signal. The text exhibits AI patterns that multiple independent systems agree on.
• One scores 80%, one scores 30%: Genuinely ambiguous. The text has some AI properties but not consistent ones across detection methods. No confident conclusion is warranted.
• Both score under 30%: Strong convergent signal of human writing.

Forgely's AI detector is free and shows sentence-level highlighting alongside the overall score. For a second opinion, GPTZero and Originality.ai are the strongest additional tools. Cross-reference results before acting on any detection outcome that matters.

Bottom line

Checking whether text is AI-generated is a two-step process: running the check and interpreting the result correctly. The first step takes seconds. The second step requires understanding that scores are probabilistic, that the inconclusive zone (30–70%) is genuinely inconclusive, that specific types of human writing systematically score higher than others, and that no detector result should be treated as a verdict without corroborating evidence.

Used correctly, AI detection is a useful first signal that prompts closer examination. Used carelessly, it produces unfair consequences for human writers whose writing style happens to match AI patterns. Know which kind of use you're making before you act on the result.