Here is how to check for AI writing words in about ten minutes: search the text for a short list of overused terms, scan for repeating sentence patterns, run one or two detectors, and read the piece out loud. No single step gives a reliable verdict. The combination catches far more than any one tool does.
This guide is written for editors, students, marketers, and anyone who has to vouch for a piece of writing. It covers the specific words and phrases that tend to give machine-written text away, free ways to find them in seconds, and what to do once you have found them. It also explains why detector scores should never be your only evidence.
What Counts as an AI Writing Word?
An AI writing word is any term a language model uses far more often than people do in the same kind of writing. The words are not wrong. They are just overrepresented, and a reader who has seen enough machine text starts to notice them.
Researchers have measured this. A 2025 study in Science Advances by Dmitry Kobak and colleagues analyzed more than 15 million PubMed abstracts and found that words such as “delves” and “showcasing” jumped in frequency after ChatGPT arrived. The team estimated that at least 13.5 percent of 2024 abstracts had been processed with a language model.
Context matters. A word like “pivotal” is fine in a history essay and suspicious in a product description for a garden hose. What you are really checking is whether a word sounds like the writer and fits the topic.
Step 1: Search the Text for Known AI Words
Start with a plain search. It takes under two minutes and finds the most obvious offenders before you spend time on anything else.
A Starter List of Words to Search
These terms show up again and again in writing flagged as machine-made, and many of them are catalogued in the Wikipedia editors’ guide to signs of AI writing:
- delve, delves, delving
- tapestry, intricate, intricacies
- pivotal, crucial, testament
- underscore, showcase, highlight (used as a verb)
- robust, seamless, vibrant
- landscape (used abstractly), realm, journey
- foster, bolster, elevate, unlock
Also search for the verb forms and plurals, since “underscores,” “showcased,” and “fostering” count too.
How to Search in Word, Google Docs, and a Browser
In Microsoft Word, press Ctrl+F (Cmd+F on a Mac) to open the Navigation pane. Type one word at a time, and Word shows the number of matches. In a browser, the same shortcut shows a match count next to the search box.
Google Docs is faster. Press Ctrl+Shift+H (Cmd+Shift+H on a Mac) to open Find and Replace, tick “Match using regular expressions,” and paste a pattern such as delv|tapestr|pivotal|testament|underscor|showcas. Docs counts every match at once, so you can see the full damage in a single search.
Do not judge by a single hit. One “crucial” in 2,000 words means little. As a rule of thumb, five or more hits from the list in a 1,000-word piece deserves a closer look.
Step 2: Look for AI Phrases and Sentence Patterns
Single words are the easy part. Models also repeat phrases and sentence shapes, and these are harder to disguise by swapping a synonym.
Look for openers like “In today’s fast-paced world,” “It’s important to note that,” and “In the ever-evolving world of.” Look for closers that restate the whole article, such as “In conclusion” or “Overall,” followed by a summary nobody asked for.
Then check the rhythm. Machine text leans on the “not only X, but also Y” construction, on lists of exactly three items, and on sentences that end in a vague participle phrase, like “…ensuring long-term success.”
Paragraph shape is another tell. Many AI drafts produce paragraphs of nearly identical length, each opening with a topic sentence and closing with a tidy takeaway. Real writers vary more, with a one-line paragraph here and a messy five-sentence one there.
Finally, look for em dashes. A draft with one in every second paragraph deserves a second look, though many human writers love them too, so treat them as a hint and never as proof.
Step 3: Run the Text Through an AI Detector
Detectors give a quick second opinion once you have your own findings. Popular options in the US include GPTZero, Originality.ai, Copyleaks, Winston AI, and the AI indicator inside Turnitin, which schools license rather than sell to individuals. Some offer a free tier or trial with a word limit.
Paste in at least a few hundred words, since short samples produce shaky scores. Run the same text through two different tools. If both agree, that is more informative than one tool shouting a number at you.
Treat the percentage as a hint, not a verdict. Each tool calculates its score differently, so a 90 on one detector and a 40 on another can happen for the same text.
What Detectors Actually Measure
Most detectors estimate how predictable the wording is. GPTZero, for example, has described using perplexity (how surprising each word choice is) and burstiness (how much sentence length and structure vary). Text that is very smooth and uniform scores as more likely to be machine-written.
Why Detectors Get It Wrong
Detectors make mistakes in both directions. OpenAI shut down its own AI Classifier in July 2023 because of its low accuracy rate. When it launched, the tool correctly flagged only 26 percent of AI-written text and wrongly flagged 9 percent of human writing as AI.
Bias is a bigger problem. A Stanford study led by James Zou, published in the journal Patterns in 2023, found that seven popular detectors misclassified more than half of TOEFL essays by non-native English writers as AI-generated. Plain, careful English from a second-language writer looks “predictable” to these tools.
Universities have noticed. Vanderbilt disabled Turnitin’s AI detector in August 2023, noting that even a 1 percent false positive rate would mean roughly 750 papers wrongly flagged each year at its size. A flag is a reason to look closer, not a finding of misconduct.
Step 4: Measure Word Frequency Against Your Own Baseline
This is the step most guides skip, and it helps most when you know the writer. Take two or three pieces the person wrote before AI tools were common, such as old emails, essays, or blog posts. Run each through a free word-frequency counter and note their most-used words.
Now run the suspect draft through the same counter. If words the person never used suddenly appear several times, and their usual phrases vanish, you have something concrete to discuss. This comparison is far harder to argue with than a detector percentage.
If you do not know the writer, compare the draft to your own style guide. Check whether the piece uses the brand’s preferred terms and the plain verbs your team normally writes. Generic text usually skips both.
Step 5: Read It Aloud and Hunt for Specifics
Reading aloud catches what searches miss. You will stumble over sentences that sound like a brochure, and you will notice when five paragraphs in a row open the same way. It takes about as long as the piece takes to read, so it costs little.
Then hunt for specifics. Human writing is full of details only someone with experience would include: a price they paid, a street name, a mistake they made, a number from their own records. AI text tends to stay general, because the model has no life behind the sentences.
Ask a few simple questions as you read. Could this paragraph appear unchanged on a competitor’s site? Does any sentence name a real person, place, date, or source? Would the writer be able to explain this claim if you asked them in person?
Putting the Checks Together
Each step gives you one signal. The word search gives you a hit count, the pattern scan gives you repeated structures, the detectors give you a probability, the baseline comparison shows a change in vocabulary, and the read-aloud shows whether specifics are missing.
As a working rule, one signal alone means very little. Two signals that point the same way justify a closer edit or a friendly conversation. Three or more is a strong sign the text was mostly machine-generated, though only the writer can confirm that.
What to Do When You Find AI Writing Words
Do not just swap in synonyms. Replacing “delve” with “dig” leaves the same empty sentence underneath, and readers still spot it. Ask what the sentence is trying to say, then rewrite it with a fact.
A sentence like “This tool plays a pivotal role in streamlining workflows” says nothing. A version with a real task and a real result says something.
If you are editing someone else’s work, start with a conversation, not an accusation. Share what you found, such as the hit count and the repeated phrases, and ask how the draft was written. Many teams now allow AI assistance as long as a human checks the facts, so the real issue is often quality and disclosure.
Words That Look Like AI but Are Not
Plenty of ordinary words get blamed unfairly. “Moreover,” “furthermore,” and “however” are old transition words that appear in student essays, legal writing, and academic papers long before chatbots existed. Flagging every one of them will wrongly accuse careful writers.
Field vocabulary matters too. “Robust” is standard in statistics and software testing, “landscape” is normal in a piece about gardens or geography, and “pivotal” is common in sports and political reporting. Judge the word against the topic before you decide it is suspicious.
Lastly, remember that people can pick up AI phrasing from what they read. A writer who spends all day with chatbot output may start using “delve” without ever pasting anything. That is another reason the word list can only ever be a signal.
Does Google Penalize AI-Written Content?
Google does not penalize text simply because a model wrote it. Its Search Central guidance from February 2023 says the company rewards helpful content however it is produced, and that using automation mainly to manipulate rankings breaks its spam policies.
In March 2024, Google also began targeting “scaled content abuse,” meaning pages mass-produced to rank with little original value, whether the author is a person or a tool. The practical takeaway for site owners is that edited, fact-checked, experience-backed content is what Google says it wants, and unreviewed bulk output is what it targets.
A Ten-Minute Routine You Can Repeat
Once you know the steps, the whole process fits in a short routine. Here is how to check for AI writing words on a typical 1,500-word article:
- Search the word list in Google Docs (two minutes).
- Scan openers, closers, and paragraph shapes (two minutes).
- Run two detectors on the same text (two minutes).
- Compare vocabulary with the writer’s earlier work if you have it (optional).
- Read it aloud and mark every sentence with no specific detail (three minutes).
Keep a running note of the words that surface in your own drafts or your team’s. Every writer has favorites, and a short personal watch list is easier to act on than a generic one. Update it every few months, since the words models overuse shift as new versions come out.
Knowing how to check for AI writing words will not tell you who wrote a piece, and it is not meant to. It tells you whether the text sounds like a person with something specific to say, which is the question readers and search engines both care about.
