Language in science has always evolved—glimpses of new terms, shifts in tone, and the slowly changing cadence of scholarly prose. But a recent study from Florida State University suggests something striking: the shifts triggered by AI-enabled writing tools aren’t just speeding up a few words here and there. They’re reshaping how scientists talk about ideas on a broad, semantic level. The authors, Riley Galpin, Bryce Anderson, and Tom S. Juzek, ask not merely which words have spiked, but how whole clusters of synonyms move together, and what that means for the clarity, nuance, and even bias of scientific writing.
The research team—anchored at Florida State University—set out to map the 구조 of language change in scientific English as it appears in abstracts from PubMed. Their aim was to move beyond a simple count of overused terms like “delve” or “crucial” and to ask whether these spikes reflect a deeper shift in meaning and usage. In other words, are we seeing a game of lexical roulette, where one word simply replaces another, or is there a more systematic realignment of how scientists express ideas, justify results, and frame significance?
To explore these questions, the team built a careful, data-driven picture. They started with a set of 32 focal words that had attracted attention in recent debates about AI-influenced writing. They then pulled all synonyms for those words, disambiguated by part of speech, and checked how those synonyms fared in the literature from 2020 to 2024. The goal was to see whether spikes in one word’s frequency came with drops in nearby synonyms (the classic replacement narrative) or whether entire semantic neighborhoods rose or fell together. The stakes aren’t small: if AI tools are nudging the scientific lexicon in broad, pragmatic directions, it could ripple through how research is read, cited, and applied.
One striking takeaway is that the shifts look more like semantic and pragmatic reweighting than simple word-for-word substitution. When a spike occurs, it tends to pull along related terms in the same semantic family. The word “crucial,” for instance, doesn’t just outrun its closest cousins; its neighbors in the semantic cloud—such as “essential” and “key”—also rise together. The researchers’ long view—28.8 million PubMed abstracts spanning nearly five decades—lets them see where these ripples sit in the larger ocean of scientific writing. And the plot isn’t a straight line. It’s a chorus: many related words climb in unison, then plateau or drift, sometimes with subtle ups and downs that tease apart timing, topic shifts, and writing style changes.
In one sense, the study is a forensic look at how language evolves under the influence of new tools. In another, it’s a high-stakes reminder of how language can shape thought. If entire clusters of words become more common because they’re favored by the automated writing aids that many researchers now use, then the way science is framed—what counts as a signifier of importance, rigor, or novelty—could shift in ways that are hard to notice at first glance. The authors emphasize that this is not a trivial quirk of vocabulary. It’s a structural change in how scientific ideas are packaged and connected to claims of impact.
Section 1
What the study looked at and how it did it is a story of careful scoping with a big data lens. The researchers began with 32 focal words that have shown notable spikes in recent literature and translated each word into its lemma form and its part-of-speech tag. This matters: the same surface form can function differently in science, depending on whether it’s a verb, an adjective, or a noun. By preserving those distinctions, the team could see how AI-augmented language might reshape not just which words are used, but how they operate within sentences.
The synoptic backbone of the analysis rests on a mix of traditional lexicography and modern data techniques. For each focal word, they pulled all plausible synonyms from a standard thesaurus, then used a language-processing step to filter those options for relevance in academic writing. They also leveraged a widely used automated language tool to assess which synonyms are most apt in an academic context. The aim was to minimize bias in synonym selection, so the subsequent trends would reflect genuine usage, not curate a preferred lexicon. With the synonyms in hand, the researchers tracked their occurrences per million words (OPM) in PubMed abstracts, spanning 1975 to 2024, turning a sprawling corpus into a measurable arc of change.
The “replacement hypothesis”—the idea that a spike in one word would simply push other words out of the way—acts as a natural baseline. If the language were simply reassigning synonyms, you’d expect a seesaw: up here, down there. But what they found, time and again, is that clusters of synonyms shift in chorus. Across the focal words—things like “crucial,” “significant,” and “underscore”—the synonyms tend to rise in tandem, painting a picture of broader semantic and pragmatic recalibration rather than a race to replace one lexical item with another. That finding points to a subtle, large-scale re-tuning of how scientists express emphasis, qualification, and nuance.
Another layer of precision comes from the part-of-speech analysis. The shifts aren’t confined to nouns; verbs, adjectives, and adverbs show most of the action. In particular, adjectives and adverbs—the small, sometimes invisible levers of tone that color a sentence—bear the brunt of the changes. This suggests that the influence of AI-augmented writing tools may be less about what scientists describe (the nouns) and more about how they describe it (the qualifiers, qualifiers of qualifiers, and the rhythm of the prose). The study’s granular POS approach is a methodological reminder that language change can be as much about how something is said as about what is said.
Section 2
Beyond the spikes, the study threads a second, quieter needle: the pattern of words that actually declines in frequency. This is the “decreasing words” side of the coin, and it’s where the authors find a more complex picture. The adjective “important,” which acts as a cousin to several focal words like “significant” or “noteworthy,” is one of the notable decliners. To understand this, the researchers built a careful procedure to identify words whose frequency drops could not be readily explained by obvious causes such as pandemic-era language, year-signal noise, or mislabeling of parts of speech. After a two-step process of independent annotators and a cross-check with additional data, they settled on a list of 13 decreasing focal words that seemed plausibly linked to AI-driven changes in usage.
What emerged from the long-term view of these decreasing words was not a neat mirror image of the spikes. Instead, they showed a more jagged pattern: large fluctuations over years, occasional sharp peaks, and sometimes gradual declines. The changes aren’t uniform across topics or subfields, which argues against a simple, system-wide “negative spike” caused by AI. Instead, the landscape looks like a terrain of climate zones—some heat, some cool, some shifting with the currents of research topics and the evolving styles of scholarly writing. The researchers also used a separate corpus experiment, prompting a randomized subset of 2020 abstracts to generate AI-authored keyword sets and then re-running the same frequency analyses. The goal was to test whether hypothetical AI-generated abstracts would reproduce the same cadence of decreases. The result reinforced the idea that the pattern is nuanced rather than a straightforward, single-cause decline.
The take-home here is not that AI is turning off a bunch of important words, but that the language ecosystem is more dynamic and multifaceted than the simple replacement story would allow. The most revealing insight is that decreases do not map cleanly onto the increases. Where the spikes are broad and cluster-like, the declines are patchy, topic-sensitive, and slower to settle. This paints a picture of language as a living system, shaped by a mixture of technology, topic drift, and human decisions about how to argue and persuade in print.
Section 3
Why do these patterns matter beyond the trivia of word counts? The authors argue that the shifts reflect something profound about how language technology interfaces with scientific prose. If AI-augmented writing tools encourage a wave of stylistic words—“crucial,” “meticulous,” “emphasize,” “underscore”—they don’t merely sprinkle these terms into existing sentences. They recalibrate the grammar of emphasis and the texture of argument. The vocabulary is not simply replacing older terms; it’s expanding the pragmatic toolbox researchers use to frame findings, convey confidence, and signal relevance. In other words, the changes are semantic and pragmatic, not purely lexical.
The implications ripple outward. If abstracts processed or shaped by AI-enabled tools carry more of these low-information, stylistic terms, readers may encounter a different balance between dense content and stylistic framing. That could affect how quickly a result is understood, how often a claim is cited, or how easily a conclusion is integrated into future work. It also surfaces questions about bias and consistency. If a subset of writing tools nudges a community toward particular styles, could that subtly suppress alternative voices or research traditions that write differently? The study doesn’t declare a verdict on ethics or desirability, but it does provide a map for further reflection on language, technology, and scholarly culture.
There’s also a candid, practical edge to the discussion. The authors acknowledge that while their data strongly suggest a semantic, pragmatic drift tied to AI-influenced writing, proving causality requires more work. They point to longitudinal analyses of spoken language, cross-domain studies, and cross-linguistic comparisons as essential next steps. They also flag an important caveat: the patterns observed in PubMed abstracts—biomedical and life sciences—may not generalize to all genres of writing. Still, the core insight—that language-influencing tools can shape not just word choice but the way ideas are framed—feels both timely and unsettling in the best possible way: it invites researchers to examine not only what they write, but how a tool might tilt the levers of expression behind the scenes.
And what about responsibility? The authors foreground the broader impact of technology companies and the learning loops that can reinforce particular stylistic preferences through reinforcement learning from human feedback. It’s a reminder that the design of writing tools doesn’t live in a vacuum; it feeds back into the way science communicates, which is, in turn, a social good. If a certain rhetorical style becomes the default because it’s easier for an AI to generate, who benefits, and who might be nudged out of the conversation? These aren’t abstract questions; they touch on the integrity of scientific communication and the diversity of perspectives that push knowledge forward.
Institutional context matters. The Florida State University team frames their work as a first pass at a new layer of analysis: not just which words spike, but how lexical neighborhoods shift and how syntax and part of speech help reveal the mechanisms of change. The headline takeaway is that AI-driven language changes are systemic, not cosmetic. If researchers want to understand exactly what this means for how science is practiced and taught, they’ll need to keep tracing the ripple effects across genres, disciplines, and languages—and keep asking who gets heard when the words around an idea are shaped by a tool rather than solely by human intent.
As the field moves forward, a quiet, almost geological question remains: will these shifts settle into a new equilibrium, or will they keep evolving as AI-assisted writing becomes even more integrated into the research workflow? The study suggests a provisional answer: language is changing in ways that resemble organic growth—clusters moving together, some elements expanding, others ebbing away, shaped by the interplay of topic, audience, and the tools used to craft text. If that’s the case, then the future of scientific English might look less like a fixed vocabulary and more like a living ecosystem, constantly rebalanced by the tools we create and the needs we have as researchers sharing knowledge with the world.
What started as a curiosity about spikes in words like “crucial” is turning into a broader inquiry about how technology and language co-evolve. That inquiry matters not just for linguists or data people, but for every scientist who writes, every reader who parses a paper, and every student learning the feel of a sharp, persuasive scientific argument. It’s a reminder that the words we reach for, and the way we arrange them, carry weight—weights that can shift as quickly as the tools we rely on to help us think and communicate.
The study is a collaborative snapshot of a moment when AI-assisted writing tools have become ordinary in the lab, clinic, and field. It invites us to watch closely not just the vocabulary that surges, but the patterns that rise with it. If we listen, we may hear a more deliberate tale about how language adapts, how science persuades, and how humanity remains the single constant as the words we use to describe our world grow a little more complex, a little more nuanced, and a touch more shaped by the tools we wield.
