AI Words to Avoid: The Complete Detector-Backed List (2026)

If you are searching for the words AI commonly uses, this is the right page: AI-generated text defaults to a specific vocabulary precisely because those words are the most predictable choices for the model, and that predictability is exactly what detectors measure. This page is about understanding the linguistic patterns that detectors and human readers associate with AI writing, and how to write more like yourself. It is not a guide to evading detection. If your assignment forbids AI assistance, the honest move is to disclose what you used and ask your instructor before you submit.

The reason a list like this is worth reading is that most “AI words” are ordinary English words. They get flagged not because they are wrong, but because a model reaches for them far more often than a person does. Knowing which words those are, and why, lets you spot the machine cadence in your own drafts. If you just want to see what fires in a specific passage, you can check your text first and read the list afterward to understand the result.

One honest caveat before the list: nobody named on this page pays me, and no placement here was bought. HumanizeMyAI runs a free humanizer and a free detector, and I will point you to both where they are the right fix, but every detector and competitor product here is cited for context, not commission.

Why Do AI Detectors Flag Certain Words?

A detector does not keep a banned-words list. It measures statistical properties of your text and compares them against what human and machine writing tend to look like. Two properties do most of the work across the field, though as the end of this section explains, not every detector uses both.

The first property is perplexity. Put simply, perplexity is how surprising your next word is, given the words before it. Language models are trained to produce the most probable next word, so their output is low-perplexity: smooth, expected, and rarely surprising. Human writing wanders. A person picks the obvious word sometimes, the odd word sometimes, and a moderately surprising word most of the time. That variety reads as higher perplexity. When a passage is unusually smooth, the detector treats low perplexity as an AI signal.

The second property is burstiness, which is the variance in your sentence lengths and structures. Human writers mix a four-word sentence against a thirty-word one. They start with a subordinate clause here, a bare subject there. Models tend to produce sentences of similar length and similar shape, paragraph after paragraph. Low variance reads as machine rhythm. This is why a paragraph full of “correct” word swaps can still fail: the words changed, but the rhythm did not.

“AI words” sit at the intersection of these two signals. A word like “delve” or “underscore” is, on its own, harmless. It becomes a signal because models overuse it, which nudges the perplexity estimate toward predictable model output, and because the sentences these words live in tend to share the same balanced, low-burstiness shape. The word is a symptom. The statistical fingerprint is the disease.

Detectors disagree about which of the two to trust, and our own is a case in point: it scores rhythm and register but leaves perplexity out on purpose. The reason is what happens under editing. When we measured both signals before and after paraphrasing the same passages, sentence-rhythm variation held almost steady while predictability fell apart. A signal that evaporates as soon as someone rewrites a draft is a weak basis for an accusation, so we dropped it. That is worth knowing as a reader too: it means a tool built on predictability alone can be defeated by an edit that changes nothing a human would notice, which is part of why two detectors so often disagree about the same page. Our free detector at /detect returns a probability, a verdict that is allowed to come back inconclusive, and a list of the named patterns it found with an example of each, so you can see which phrasings prompted the score rather than just the number.

The Full List of AI Words to Avoid

The ten words detectors flag most are delve, underscores, showcasing, leverage, intricate, pivotal, realm, tapestry, meticulous, and the phrase crucial role in shaping.

The cleanest public evidence that models overuse specific words comes from Dmitry Kobak and colleagues, whose study tracked word frequencies across more than 15 million biomedical abstracts before and after ChatGPT’s release. It circulated as a preprint in June 2024 (arXiv:2406.07016) and was published in Science Advances on July 4, 2025 ( DOI 10.1126/sciadv.adt3813). Their annotated word list is public and MIT-licensed, so anyone can check a term against it directly ( berenslab/llm-excess-vocab). They identified a set of “excess words” whose usage spiked sharply once large language models entered common use. These are not rare or technical terms. They are everyday verbs and adjectives that models reach for far more often than human authors did before 2023.

The table below pairs the headline frequency findings from that study with the broader pattern categories I track across our own corpus of 2,590 real student essays. The right column explains why a model defaults to each word; the signals are relative overuse, not a banned threshold.

The middle column is deliberately uneven, and it is worth saying why. Four rows carry a measured multiple, because the study reported one for those words. Most of the rest are marked as style-tagged entries from the same published list, which annotates each of its 900 words as either topic vocabulary or style vocabulary but does not attach a per-word figure to every one of them. We could derive our own ratio from the released yearly counts, and when we tried it as a check our arithmetic landed close to the published figures. Close is not the same, though, and a number we calculated is not a number the authors reported, so those rows stay unnumbered rather than borrow authority they have not earned.

One more caveat, and it matters more than the missing decimals. That list was measured in biomedical abstracts. A word that is genuinely excess in an oncology paper is not automatically a problem in a history essay or a cover letter, and a few entries here are simply the normal vocabulary of certain fields. If you are writing about ecology, landscape may be the literal subject. If you are writing up research design, methodologies may be the correct plural. Treat the table as a list of words worth a second look in your own register, not as a set of words that are banned everywhere.

A useful test: read each word aloud and ask whether you would say it to a friend. “I dug into the data” is something a person says. “I delved into the multifaceted tapestry of the data” is something a model writes. The further a word sits from your natural speech, the more it raises the gap a detector measures between predictable output and human variety.

AI Phrases to Avoid: Transition Words and Their Replacements

Transition words are the single most recognizable AI tell, because models use them to glue sentences together at a rate human writers rarely match. A person varies their connectives or drops them entirely, letting two sentences sit side by side. A model tends to open consecutive paragraphs with “Moreover,” then “Furthermore,” then “Additionally,” producing a metronomic rhythm that both readers and detectors notice.

The pattern to internalize is subtraction, not substitution. Most AI transitions can be deleted with no loss of meaning. When you do need a connective, the conversational one (“so,” “but,” “and,” “plus”) almost always reads as more human than the formal one. A note for non-native writers: some of these formal connectives are taught as good academic style in English-as-a-second-language curricula and are explicitly rewarded on exams like TOEFL, which is exactly why detectors over-flag careful ESL prose. I cover that bias in the academic essays section below.

Which Adjectives and Verbs Does AI Overuse?

Models reach for adjectives that sound impressive and mean very little. The giveaway is that the adjective could apply to almost anything. A “robust framework,” a “seamless experience,” a “dynamic ecosystem,” a “multifaceted challenge”: strip the noun away and the adjective tells you nothing concrete. Human writers tend to be specific, because they are describing something they actually saw. Models tend to be vague, because they are predicting the most likely impressive word.

Adjectives to watch in your own drafts include robust, seamless, multifaceted, dynamic, vibrant, comprehensive, holistic, intricate, and pivotal. The fix is not a thesaurus. The fix is to ask what you actually mean. “A robust system” usually means “a system that did not crash under load.” Write that. Specificity is the opposite of the AI signal, because a concrete detail is, by definition, less predictable.

The verb side of this pattern is just as recognizable. Models favor a small set of abstract verbs that float above any real action: underscore, foster, leverage, delve, navigate, harness, utilize, facilitate, and showcase. “We foster collaboration” is model writing. “We eat lunch together and argue about the roadmap” is human writing. “Navigate the complexities” is a phrase that appears in machine output across every topic, because it commits to nothing. When you catch an abstract verb, replace it with the plain verb for the thing that physically happened. “Utilize” becomes “use.” “Facilitate a discussion” becomes “run a meeting.” The plainer verb carries more information and reads as more human at the same time.

Generic Importance Language and Formulaic Framing

There is a whole family of phrases whose only job is to announce that something matters, without saying anything about it. These are among the strongest single-phrase AI tells, because they are formulaic: the model has learned the exact string. GPTZero has reported that the phrase “crucial role in shaping” appears roughly 182 times more often in AI text than in human writing, which tells you how locked-in these templates are.

Watch for “plays a crucial role,” “plays a vital role,” “it is important to note,” “it is important to remember,” “in today’s fast-paced world,” “in the realm of,” “when it comes to,” and “at the end of the day.” Each one is filler that a confident human writer would skip. If a sentence opens with “It is important to note that X,” the sentence is almost always stronger as just “X.” The framing phrase adds words and removes nothing, which is the textbook low-perplexity pattern: maximally predictable, minimally informative.

The deeper habit these phrases reveal is that models hedge toward importance instead of asserting a point. A person writes “This breaks the build.” A model writes “It is worth noting that this may play a significant role in potential build instability.” When you find yourself announcing that something is important, delete the announcement and let the thing be important on its own.

Is the Em Dash a Sign of AI? What the Data Shows

Short answer: no, not on its own. We measured it, and a heavy em-dash habit points at which model wrote a passage rather than at whether a model wrote it. As a human-versus-AI signal by itself the character performs at chance, which is why our detector does not score it. The longer version is below, along with the two typographic marks that do carry a little weight.

This is the category most word-focused guides miss entirely, and it is also the one where the popular advice is most confidently wrong. The signal here is not vocabulary. It is punctuation and special characters, on the theory that models place certain typographic marks at rates a person at a keyboard rarely matches. Some of that holds up. The most famous example does not, and we can be specific about it because we tested it rather than repeating it.

The em-dash is the headline case. In a study we published in June 2026, five language models answered the same 102 prompts and we measured nothing but the style of the answers. The dash turned out to be one of the sharpest dividing lines in the entire feature set, but it does not divide human from machine. It divides one vendor from another. Claude produced roughly five times as many em-dashes as GPT did, on the order of 5.4 per thousand words against 1.1. Asked to separate human writing from machine writing on its own, the character performs at chance.

That has a blunt consequence for the advice you will find on nearly every other page about this. Stripping the dashes out of your draft does not make it read as human. It makes it read as not-Claude, which is a narrower thing, and it is pinned to one company’s house style in one particular year. Our own detector does not score the character at all, for precisely this reason, and the full method, the corpus, and the code are open if you want to check the finding rather than take it from us: every model has an accent.

There is still a reason to watch your dash count, and it is the ordinary editorial one. A paragraph studded with them is simply harder to read: “The data was clear—the model overfit—and the result, while interesting—was not reproducible.” If you reach for the dash because you want the interruption it creates, keep it. What is not worth your time is budgeting the character in the belief that something is counting them.

Curly quotation marks are the second artifact. Models and rich-text editors emit typographic quotes (the angled marks and the curly apostrophe) automatically, whereas a person typing in a plain text box usually produces straight quotes. A document where every quote and apostrophe is curly was very likely composed in a tool that auto-formats, which is consistent with paste-from-model behavior. This is weak on its own but adds up alongside other signals.

The third artifact is the ellipsis. Models use the three-dot ellipsis (the single character, or a tidy set of three periods) to trail off or to soften a list. Human informal writing tends to over-produce dots irregularly, or to skip the ellipsis entirely in formal prose. A clean, evenly spaced ellipsis in otherwise formal text is a small machine fingerprint. None of these typographic signals decides a verdict by itself, but they are easy to clean, and they are the kind of detail that distinguishes a careful self-edit from a copy-paste.

What Structural Patterns Make Writing Read as AI?

Once you move past individual words, the structural tells are what survive paraphrasing, which is exactly why a synonym swap so often fails to change a detector’s mind. These patterns live in the shape of the sentence, not its vocabulary.

The “not only X but also Y” construction is the clearest one. Models love balanced correlative pairs: “not only efficient but also scalable,” “both innovative and practical.” Human writers use the construction occasionally; models use it constantly, because it is a high-probability template that produces a satisfying rhythm. When you see two or three of these in a single passage, the cadence itself is the signal.

List-first symmetry is the second pattern. A model tends to introduce a topic, then deliver a tidy three-part list where every item has the same grammatical shape and similar length. Real human lists are lopsided. One item runs long because the writer had more to say about it; another is a fragment. The machine list is suspiciously even. The three-item cadence is so common in model output that “rule of three” prose, where ideas arrive in evenly weighted triples again and again, becomes its own tell. The fix for all of these is asymmetry. Let one sentence run long and the next be three words. Let a list have a two-word item next to a full-sentence item. Deliberate unevenness is the structural opposite of low burstiness, and it is the part of the AI fingerprint that vocabulary edits cannot reach.

ChatGPT Words and Phrases to Avoid

Some tells are characteristic of ChatGPT in particular, because they come from how the assistant is tuned to respond rather than from base-model statistics. If your draft was generated in a chat interface, these are the first things to look for.

The adverbial openers are the most common: sentences and paragraphs that begin with “Certainly,” “Notably,” “Importantly,” or “Interestingly.” These are conversational throat-clearings the assistant uses to sound engaged. Human essay prose rarely opens a paragraph with a bare “Notably,” so a cluster of these reads as chat residue. Cut the opener and start with the actual point.

The second pattern is literal chatbot residue: phrases that belong to a conversation, not a document. “As an AI language model,” “I hope this helps,” “Certainly! Here is,” “Let me know if you would like,” “In summary, here are the key points.” These are unmistakable, and they survive a surprising amount of careless copying. Search your draft for “I hope this helps” and “as an AI” before you submit anything; finding them is common and embarrassing, and removing them is instant.

The third is the framing sandwich: a model answer often opens by restating the question, delivers the content, and closes by restating what it just said. Essays and articles written by people rarely repeat their own thesis as a bookend in this mechanical way. If your draft opens with “There are several reasons why X is important” and closes with “In conclusion, X is important for several reasons,” you are looking at the chat template. Keep the content, delete the bookends, and the prose immediately reads as more human.

AI Words in Academic Essays and the ESL False-Positive Caveat

In an academic essay, flagged AI vocabulary clusters in three zones: the introduction, the topic sentences, and the conclusion, and cleaning those zones removes most of the obvious signal.

Students are the readers most likely to land here in a panic, so this section is for the academic case specifically, and it carries the most important caveat on the page.

In an academic essay, the flagged vocabulary tends to cluster in three places: the introduction (where “in today’s world” and “plays a crucial role” announce the topic), the topic sentences (where “Moreover” and “Furthermore” chain paragraphs together), and the conclusion (where “In conclusion” and “it is important to remember” restate the thesis). If you wrote with AI assistance and your assignment permits it with disclosure, cleaning these three zones removes most of the obvious signal. If your assignment does not permit AI assistance, the right path is to talk to your instructor, not to launder the text.

Here is the caveat that the other guides leave out. Some of these “AI words” are natural, correct, well-taught academic English for non-native writers. Formal connectives like “moreover” and “furthermore,” and measured hedges like “it is important to note,” are drilled into English-as-a-second-language curricula and rewarded on proficiency exams. That creates a real injustice: detectors over-flag fluent ESL prose precisely because it uses the careful, formal register that ESL instruction teaches. The peer-reviewed evidence is stark. Liang and colleagues at Stanford (2023, Patterns, DOI 10.1016/j.patter.2023.100779) found detectors flagged 61.3% of TOEFL essays written by real human ESL students as AI. The 5.19% native-English comparison often quoted alongside it appears only in the study’s preprint, not the published paper, so the DOI-confirmed 61.3% figure is the number to rely on.

If you are a non-native English writer, do not read this page as a verdict that your natural vocabulary is wrong. Read it as an explanation of why a detector may be treating your authentic writing unfairly. I cover the bias and how to document it for an academic appeal in our ESL detector bias review, and the broader picture of how detectors over-flag non-native writers in our ESL and AI detection guide. The published Stanford figure is the strongest peer-reviewed evidence you can bring if your own writing gets flagged.

Why Avoiding These Words Will Not Stop an AI Detector

Here is the hard truth that follows directly from the detector mechanism. You can scrub every word in every table on this page and still fail a detector. I have watched it happen in testing dozens of times.

The reason is that detectors score perplexity, burstiness, and structure, not a token blacklist. Swapping “delve” for “look at” changes one word, but if the surrounding sentences keep their uniform length and their balanced, low-surprise shape, the structural fingerprint is intact and the verdict often does not move. Word-level edits address the most visible symptom while leaving the underlying statistical pattern untouched. This is also why basic synonym-swap paraphrasers underperform: they preserve the word-by-word probability distribution and the sentence rhythm, so the very signals a detector relies on stay in place.

Genuinely changing a detector’s read requires structural rewriting: varying sentence length deliberately, breaking the three-item cadence, letting some sentences be short and blunt and others run long, and introducing the lexical unpredictability of real human choice. That is hard to do by hand under deadline, and it is the actual job our humanizer is built for. HumanizeMyAI is trained on 2,590 real student essays rather than a synonym dictionary, which is why it rewrites the structure and rhythm of a passage instead of just substituting words. If you have a draft that reads as machine-written and you have a legitimate use case for the rewrite, you can humanize it here on the free tier (a free account includes four rewrites of 250 words each, no card), and for a side-by-side of which tools actually move the structural signal rather than just the vocabulary, our humanizer comparison has the test data. For the Turnitin case specifically, the Turnitin AI detection guide explains the layered classifier these patterns feed into, and our bypass-Turnitin walkthrough covers the legitimate-writer and ESL false-positive scenarios.

Check Your Own Text Before You Submit

Reading a list is useful. Seeing the list fire in your own writing is what actually changes a draft.

Before you submit anything that matters, paste your text into our free detector at /detect. It is pattern-based and honest: instead of returning a single mystery percentage, it highlights which of the patterns from this page appear in your passage, so you can see whether you failed on vocabulary, on punctuation artifacts, on sentence rhythm, or on a chat-residue phrase you forgot to delete. You get four checks per day with no account. Run a passage, read which patterns lit up, fix those specific things, and run it again to confirm the change. That feedback loop teaches your ear faster than any word list, because you stop guessing and start seeing the machine cadence in your own voice.

The goal of this whole page is not to help anyone cheat a detector. It is to help you recognize the patterns that make writing sound like a machine, so that whether you are an essayist, a content writer, or a non-native English speaker fighting an unfair flag, your prose reads as what it is: yours.