ChatGPT Marker Words That Reviewers Notice: AI Vocabulary Detection Guide 2026

“Marker words” is the term researchers use for the vocabulary fingerprint AI models leave behind. This guide covers ChatGPT marker words and other AI-generated text patterns that professors and automated detectors flag. When ChatGPT or similar tools write an essay, they tend to overuse certain words: “delve,” “underscore,” “tapestry,” “robust,” “comprehensive.” These aren’t random. They show up in patterns that professors and AI detectors recognize consistently.

Picture this: it’s 11pm on a Tuesday. You’ve got a history paper due Thursday. You asked ChatGPT to help you draft the introduction, and the words “delve” and “robust” showed up without you realizing it. You copy-paste it into your draft, and your professor reads it back next week. “This sounds generic,” they say. You don’t know why they think so. The marker word pattern is there. It’s just invisible until someone points at it.

A single marker word won’t flag your paper. But when they cluster together, with two or more tier-1 words plus a scatter of tier-2 words in the same paragraph, reviewers start to catch it. The research is clear. Marker words follow measurable statistical patterns, not guesswork. Kobak et al. (2025) analyzed 15 million PubMed abstracts and found that “delve” appeared 28 times more often in 2024 than the previous trend predicted. “Underscore” jumped 13.8 times. This isn’t a suspicion. It’s a data point.

This guide covers the evidence behind the marker-word lists, breaks down which words reviewers notice, and gives you practical replacements you can start using right now. We’ll also walk through natural academic writing techniques that replace AI vocabulary without adding more buzzwords.

The key insight: professor AI detection works through AI writing patterns, not word lists alone.

The Most Commonly Flagged ChatGPT Marker Words (and How Severe They Are)

AI vocabulary detection goes beyond memorizing lists. AI detector vocabulary includes tier-1 kill-on-sight words, cluster-tier words that appear in combinations, and light-signal terms that only become suspicious when stacked. PaperBleach (2026) and SlopDetector (2026) both use a classification system that matches what reviewers actually react to. The tiers are grounded in corpus studies (Kobak et al. (2025)) and reader-reaction data (Juzek & Ward (COLING, 2025)).

Key AI Vocabulary Detection Findings

The table below shows the classification framework in full. It is what I use when I’m editing a draft. It turns a vague worry into a concrete checklist.

AI Vocabulary Classification by Severity

Kobak’s study quantified the excess vocabulary for the top flagged words:

Excess Vocabulary Trends

The data from Figure 1 in Kobak et al.’s paper visually demonstrates the post-ChatGPT vocabulary spike. The bar chart shows frequency of marker words with counterfactual extrapolation lines that make the spike unmistakable. It’s the strongest piece of evidence the field has for why “delve” and similar words became so prevalent. Credit: Dmitry Kobak et al. (Science Advances, 2025).

This finding confirms what many students already suspected: AI buzzwords academic writing follow patterns, not randomness.

What the tiers mean in practice:

• Kill-on-sight: One of these in a sentence is enough to make a reviewer pause. If you use “delve” twice in an essay, it’s a problem. If you use “tapestry” once, it’s still suspicious.
• Cluster-tier: A single “robust” is fine. Three tier-1 words plus a scatter of tier-2 words in one paragraph is a tell. The cluster, not a single word, is the red flag.
• Light-signal: “Realm” and “landscape” appear naturally in academic writing. They become a problem when they’re stacked with other AI vocabulary in the same paragraph.

How to Use the Classification Framework

When editing your own draft, go through the three tiers in order. Check every sentence for kill-on-sight words first. If you find any, replace them immediately. Then scan for cluster-tier words and count them. If you have two or more in one paragraph, cut the extras. Finally, check for light-signal words stacked together. If they’re alone, they’re fine.

Words by Category

Verbs: These are the most heavily flagged category. “Delve” tops every list, followed by “underscore,” “foster,” “navigate,” and “showcase.” Juzek & Ward’s reader-reaction data showed participants rated “delve” more negatively than other buzzwords, likely because it carries a specific kind of formal authority that reads as inauthentic.

Adjectives: “Robust,” “comprehensive,” “meticulous,” “pivotal.” These are formal synonyms for common words. “Robust” means strong. “Comprehensive” means thorough. “Meticulous” means careful. The problem is that a student who would say “strong” or “thorough” in a draft suddenly reaches for the fancy version the night before a deadline.

Nouns: “Tapestry,” “landscape,” “realm,” “ecosystem.” These are placeholders. “The digital landscape” means nothing concrete. “The realm of marketing” is a topic label disguised as insight.

Transitional phrases: “It is important to note,” “furthermore,” “moreover,” “in conclusion.” These are not wrong words. They’re the kind of thing you write when you’ve memorized a formula but haven’t thought through your argument yet.

Why AI Overuses These Words (It’s Not a Glitch, It’s a Feature)

AI models don’t “choose” these words by accident. The overuse traces directly to how they were trained, and the mechanism is well-documented.

RLHF is the root cause. RLHF stands for Reinforcement Learning from Human Feedback. In simple terms: AI models were trained to sound authoritative and thoughtful, but the raters who evaluated the outputs preferred formal, academic register. Juzek & Ward (COLING, 2025) at Florida State University showed that human preference raters consistently rated formal-sounding text as higher quality. That meant the model learned “delve” reads as thoughtful, so it overreached. The model didn’t learn that “delve” is a useful word. It learned that “delve” scores higher on human-preference evaluations.

This explains two things we see consistently:

1. Marker words cluster around formal register. Words like “delve,” “underscore,” “primarily” aren’t random picks. They’re the kind of vocabulary raters rewarded because it sounded authoritative.
2. The overuse is systematic, not scattered. Kobak et al.’ (2025) study of 15 million PubMed abstracts didn’t find random word choice. It found a quantifiable excess pattern: “delve” appeared 28 times more often than the prior trend predicted. That’s not noise. That’s a trained behavior.

Reader-reaction data confirms it. Juzek & Ward asked participants to read passages with and without AI vocabulary. They found that readers consistently rated the same passage as “better” when marker words were removed, even though the meaning was identical. “Delve” didn’t add insight. It added weight that readers could feel.

Juzek & Ward, COLING 2025. Full study: https://aclanthology.org/2025.coling-main.426.pdf

The takeaway for your writing: AI overuses these words because it was trained to reward formal register. That means the fix isn’t memorizing a blacklist. It is recognizing that formal does not always mean better, and choosing the simpler word when they mean the same thing.

How Professors Detect AI Writing

Professors don’t look at word lists the way a detector does. They look at pattern stacks.

CoGrader (2024) identified five signals professors combine:

1. Overused vocabulary (the tier-1 and tier-2 words from the table above)
2. Prefab phrases (“It is important to note,” “in conclusion”)
3. Uniform sentence lengths (every sentence roughly the same length)
4. Thin substance (claims without concrete evidence or examples)
5. Fake authority (“As many experts have noted…” without citing a specific expert)

No single word is proof. But when all five appear together, a professor’s brain says: this is not my student.

Pattern stacks matter because human brains are pattern-matchers. Russell et al. (ACL, 2025) found that infrequent AI users detected AI at chance level, while frequent users were accurate detectors. The more experience readers had with AI text, the better they got at spotting it. That’s the key insight: pattern recognition works when you’ve seen enough of the signal.

How professors actually do it, practically:

• By week 6 of a semester, most professors compare your current writing against your past work. A sudden quality jump or vocabulary shift is a red flag.
• They read for substance. AI can summarize a source. It can’t usually synthesize your course materials with a specific insight from a lecture only they heard.
• They notice structure. AI writing tends toward five-paragraph symmetry with a predictable thesis-body-conclusion cadence. Real student writing is messier.

The combination is what matters. A single marker word is evidence. Five markers from different categories, plus thin substance, plus uniform sentence structure. That is a pattern stack. And pattern stacks are what get flagged.

Legitimate Academic Vocabulary vs. AI Buzzwords in Academic Writing

This is where the distinction matters most for your writing. Not all formal vocabulary is AI vocabulary. The difference between AI buzzwords in academic writing and legitimate academic terms determines whether your writing reads as authentic or generated. Not all formal vocabulary is AI vocabulary.

Legitimate academic vocabulary is discipline-specific, precise, and context-dependent.

• “The concentration ratio fell by 12% in 2023”
• “The study used a double-blind RCT design”
• “The sample size (n=4,500) exceeded Cohen’s (1988) threshold”

AI vocabulary is formal, vague, and substitutable across topics.

• “The realm of marketing” (replace with the specific field)
• “The digital landscape” (replace with a concrete description)
• “The intricate tapestry of modern society” (replace with evidence, not labels)

The difference is concrete meaning versus empty tone. Legitimate academic vocabulary carries meaning. AI vocabulary carries style.

This matters for advanced students and ESL writers. Non-native speakers often use formal vocabulary because they haven’t fully internalized colloquial English. Liang et al. (Patterns, 2023) found that AI detectors flagged over 61% of TOEFL essays as AI-generated. That’s a known false-positive risk: the patterns AI detection tools look for overlap with non-native writing patterns, especially for students who use simpler vocabulary and structured sentence forms.

Think of it this way: you’re an ESL student who spent weeks on a literature essay. You reached for “robust” and “comprehensive” because those are the words you know work well in academic assignments. Your professor reads the essay and asks, “Is this yours?” You’re not using AI tools, but the detector thinks you are. That’s the bias Liang’s study documented: the same detectors that flagged 61% of human TOEFL essays were near-perfect on native speakers. The problem isn’t your writing. It’s that the tools confuse formal register with artificial generation.

What to watch for: if your writing sounds like it came from a thesaurus rather than from thinking, you may be pushing past legitimate academic vocabulary into AI territory. The test is the concrete-meaning rule: if you can replace a fancy word with a common one without losing meaning, you’re using AI vocabulary.

Practical Replacements (Before and After)

This is the editing section. Here’s how to replace AI words with cleaner prose.

CoGrader (2024) published concrete before/after examples for history, ELA, and science assignments. The pattern is consistent across all three: replace vague academic-sounding words with precise, discipline-specific alternatives.

Verb replacements

“Delve” → “Examine” or “Analyze”

• Before: “This paper delves into the causes of the Civil War.”
• After: “This paper analyzes the economic and social causes of the Civil War.”

The “delve” swap isn’t just word replacement. The better version adds concrete specifics (economic and social) that a marker-word swap alone doesn’t provide.

“Underscore” → “Show” or “Emphasize”

• Before: “The data underscores the importance of early intervention.”
• After: “The data shows that children who receive early intervention score 12% higher on reading tests.”

Notice the second version adds concrete detail. That’s the fix. “Show” replaces “underscore.” But the deeper fix is adding specifics.

“Foster” → “Encourage” or “Build”

• Before: “Teachers foster a learning environment.”
• After: “Teachers encourage critical thinking through Socratic questioning.”

Adjective replacements

“Robust” → “Strong” or “Detailed”

• Before: “The study presents a robust analysis.”
• After: “The study presents a detailed analysis of 2,400 student responses.”

“Robust” is a placeholder word that sounds formal but means nothing concrete. “Detailed” at least implies the work. “Detailed analysis of 2,400 student responses” is proof.

“Comprehensive” → “Thorough” or list what’s included

• Before: “A comprehensive review of the literature.”
• After: “A review of 47 studies published between 2010 and 2024.”

Again, the concrete detail is what matters. The adjective is just a label. The numbers are the substance.

“Meticulous” → “Careful” or “Precise”

• Before: “The researchers conducted a meticulous examination.”
• After: “The researchers followed a step-by-step protocol with peer-reviewed checks.”

Noun replacements

“Tapestry” → Cut it entirely. Sometimes the right fix isn’t a swap. It’s deleting the phrase. “The intricate tapestry of cultural practices” means nothing. No synonym exists for this because the phrase itself is the problem. Cut it. Describe the specific practices you’re discussing instead.

“Realm” → Name the field

• Before: “The realm of artificial intelligence.”
• After: “Machine learning in healthcare diagnostics.”

“Realm” is a placeholder. Name the specific subfield you’re discussing.

Transition phrase replacements

“It is important to note” → Cut.

• Before: “It is important to note that the study has limitations.”
• After: “The study has limitations: sample size was small (n=150), and follow-up was only three months.”

The second version actually states the limitations. The first is filler.

“In conclusion” → Cut or replace with a summary sentence.

• Before: “In conclusion, the findings underscore the importance of policy reform.”
• After: “Policy reform could reduce costs by 15%, but implementation requires state-level coordination.”

The first version stacks two marker words (“underscore” + “in conclusion”) and says nothing concrete. The second version states the actual finding and a specific obstacle.

CoGrader (2024). Educator guide with categorized lists and examples: https://cograder.com/content/ai-plagiarism/

The “cut, don’t swap” rule

PaperBleach (2026) recommends a third option beyond swapping: sometimes the right fix is removing the entire phrase. “The intricate tapestry of cultural practices” — no synonym exists for this because the phrase itself is the problem. Cut it. Describe the specific practices you’re discussing instead.

PaperBleach (2026). Tiered classification and editing guidance: https://paperbleach.ai/post/ai-vocabulary-blacklist-delve-tapestry-realm

The Perishability Problem (Why This List Changes)

Marker-word lists are not permanent. The first thing students learn about AI vocabulary detection is that the list they’re reading today will look different in six months.

Geng & Trotta (Findings of ACL, 2025) reported a marked drop in “delve” usage after it was publicly identified in early 2024. When “delve” became famous as an AI marker word, researchers and content filters started flagging it, and models were fine-tuned away from the behavior. The result: “delve” already dropped off the marker-word list.

What this means for your writing:

• Don’t memorize a blacklist and stop reading. The list changes.
• Focus on the pattern, not the word: vocabulary density + uniform structure + thin substance.
• Watch for clusters, not single words. One marker word is fine. Three is a problem.

The perishability insight makes this article fresh, but it also means you need a framework that outlasts individual words. The cluster-and-structure approach (vocabulary + sentence length + substance) matters far more than memorizing “don’t use delve.”

Next Steps

AI vocabulary is a fingerprint. It is not a mystery, and it is not luck. Marker words follow measurable patterns. Excess vocabulary that corpus studies can quantify, cluster behavior that readers detect, and structural signatures that professors recognize.

The practical takeaway is straightforward:

• Eliminate marker-word clusters rather than memorizing a blacklist.
• Replace vague formal words with precise, discipline-specific alternatives.
• Vary sentence lengths naturally.
• Ground claims in concrete evidence from your course materials.

If you want deeper guidance on writing authentically, covering how detectors actually work, ESL false-positive risks, and step-by-step editing techniques, read our guide on writing authentically which breaks it all down in more detail.

Bottom line: Your writing will look better after you fix marker words, not just because you’ll avoid detection. Concrete words, varied structure, specific evidence make any essay stronger. The AI detection benefit is just a side effect of better writing.

Group projects make this even more tangled. You hand in the section you wrote — careful, specific, your voice — but the rest of the group’s paragraphs are polished to that same AI sheen. Your professor sees the whole thing as one voice. “This sounds generic,” they say. You didn’t write the word “delve,” but you can’t explain why the marker word pattern is everywhere. The fix isn’t just editing your own prose. It’s recognizing that AI vocabulary follows patterns, and those patterns survive team collaboration.