THE INFLUENCE OF AI (CHATGPT) ON THE EVOLUTION OF THE ENGLISH LEXICON: A LINGUISTIC PERSPECTIVE

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THE INFLUENCE OF AI (CHATGPT) ON THE EVOLUTION OF THE

ENGLISH LEXICON: A LINGUISTIC PERSPECTIVE

Mukhiddinova Rushana Mirkomilovna

1

st

year Master’s student of the Foreign languages and literature faculty, Asia

Technologies University, Uzbekistan

E-mail:

Muhiddinovarushana@Gmail.Com

Scientific advisor: PhD., assoc.prof.,

Sulaymonov Bobur Nodir ugli

Abstract

. This paper explores the profound influence of artificial intelligence,

specifically ChatGPT, on the English lexicon through the introduction of
neologisms, semantic shifts, metaphorical extensions, and discourse-level
transformations. As AI-powered conversational agents enter mainstream usage, their
linguistic output and the cultural environment they help create foster the emergence
of new lexical items and novel usages of existing words. Drawing upon corpus
linguistics, semantic field theory, and discourse analysis, the study identifies key
lexical innovations associated with ChatGPT and explores their integration into
academic, media, and informal communication. The findings shed light on how
technology is not only shaping language use but also influencing the conceptual
frameworks through which we interpret communication, knowledge, and human-
machine interaction.

Key words:

AI, ChatGPT, English lexicon, semantic shift, neologism,

metaphor, lexical innovation, discourse analysis, digital linguistics.

Introduction.

Language, as a dynamic and adaptive system, evolves in

response to technological, social, and cultural forces. The advent of artificial
intelligence (AI), particularly conversational models like OpenAI's ChatGPT, has
sparked one of the most significant linguistic transformations of the 21st century.
While the internet introduced global linguistic convergence, AI marks the beginning
of real-time language co-construction between humans and machines. ChatGPT is
more than a tool

–

it is a co-participant in dialogue that mimics human conversational

logic and creativity.

In this paper, we examine how ChatGPT contributes to the development of

the English lexicon by:

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1. Facilitating the creation and dissemination of neologisms;
2. Triggering semantic shifts in existing words;
3. Popularizing domain-specific jargon;
4. Altering communicative norms and stylistic conventions.
This research also considers the sociolinguistic consequences of AI-generated

language, such as lexical democratization, shifts in digital identity, and the cultural
authority of machine-generated discourse. The questions guiding this study are:

What linguistic features characterize the emerging AI-influenced lexicon?
How do new terms enter and circulate within public discourse?
What theoretical frameworks best capture the mechanisms of AI-induced

language change?

Methodology.

To capture the multi-dimensional impact of ChatGPT on the

English lexicon, the research employed a triangulated methodology incorporating
corpus linguistics, semantic field mapping, and discourse analysis. Corpus
Compilation: A custom corpus was compiled from over 1,000,000 words of digital
text produced between 2020-2024. This included:

OpenAI documentation and technical blog posts
Reddit threads on r/LanguageTechnology and r/ChatGPT
Media articles from BBC, Wired, MIT Technology Review
Educational platforms (Coursera, edX, Khan Academy)
YouTube transcript data from AI explainers
English language textbooks referencing AI concepts
Lexical Analysis. Using AntConc and Sketch Engine, lexical frequency,

collocation, and co-occurrence patterns were analyzed. Word clusters and bigrams
were identified to detect emerging terminologies.

Semantic Field Theory. The study mapped emerging AI terms onto semantic

fields to understand conceptual shifts. For instance, traditional terms from
psychology (e.g., “hallucination,” “memory,” “alignment”) were tracked in their AI
recontextualizations.

Discourse Analysis. A sample of 300 discourse events was analyzed using

Fairclough’s critical discourse model to evaluate how AI-related terminology is
framed in media, education, and online forums. Focus was placed on metaphorical
framing, agentivity, and narrative positioning.

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Results.

The findings reveal three primary vectors of lexical evolution

influenced by ChatGPT:

Emergence of New Lexical Items: ChatGPT has introduced dozens of

neologisms that encapsulate novel technical processes or user experiences.
Examples include:

Prompt engineering: The strategic design of inputs to manipulate AI

responses.

Hallucination (AI): The generation of false or misleading content.
Token limit: A constraint on text length in AI processing.
Prompt injection: A method to override AI model instructions.
Zero-shot/few-shot learning: Model training modes described metaphorically.
These terms appear increasingly in non-technical contexts such as business

meetings, online education, and casual discourse.

Semantic Shift and Polysemy. Many existing English words have developed

new AI-specific meanings. For example:

Alignment: From moral or ideological conformity → ensuring AI goals match

human intentions.

Training: From physical or human cognitive skill acquisition → machine

learning process.

Bias: From social prejudice → statistical irregularity in data.
These shifts represent not just lexical change but conceptual reframing, where

AI discourse reshapes how speakers understand familiar words.

Metaphorical Innovation and Reframing. AI-related language heavily relies

on metaphor to explain abstract processes. For example: “ChatGPT hallucinates”
anthropomorphizes the model, attributing cognitive behavior. “We must align AI”
evokes political or moral alignment. “A model's memory is limited” humanizes a
technical limitation.

These metaphors influence public perception and ethical debates by framing

machines as agents capable of intent, memory, and responsibility.

Discussion.

The linguistic impact of ChatGPT can be understood as a

convergence of technological acceleration, user creativity, and semantic need. The
language around AI is not imposed top-down by engineers; rather, it emerges
through collective interaction with the technology. Three key implications arise.

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Lexical Democratization and Global Circulation. AI discourse breaks

traditional barriers between technical jargon and general language. ChatGPT users
without coding backgrounds now routinely use terms like “fine-tune,” “language
model,” and “inference.” This democratization fosters wider tech literacy but also
creates barriers for those outside digital cultures.

Discourse Authority and Machine Agency. The use of authoritative or

anthropomorphic language grants ChatGPT perceived agency in communication.
Phrases like “ChatGPT said” or “it knows” suggest subjectivity, despite the tool’s
lack of consciousness. This has implications for education, misinformation, and
human-machine trust dynamics.

Theoretical Implications for Linguistics: Traditional models of lexical change

emphasize slow, community-driven innovation. ChatGPT demonstrates a hybrid
model, where lexical items are simultaneously generated by centralized AI
development and decentralized user interaction. This challenges the dichotomy
between prescriptive and descriptive language evolution.

Furthermore, the process echoes Wittgenstein’s language games, where

meaning arises through use. As users interact with ChatGPT, new language forms
are tested, adopted, or rejected in real time, forming a new kind of socio-linguistic
feedback loop.

Conclusion.

AI technologies, particularly ChatGPT, are actively shaping the

evolution of the English lexicon. This influence extends beyond jargon into core
semantics and cultural frames of reference. The interaction between human
creativity and AI response has become a catalyst for rapid lexical innovation.

Understanding this phenomenon requires an interdisciplinary approach

–

combining linguistics, cognitive science, discourse theory, and AI ethics. Educators,
policymakers, and language technologists must engage with the implications of AI-
mediated language change, ensuring that linguistic inclusivity, accuracy, and critical
thinking remain central in an era of machine-influenced communication.

Future studies should explore cross-linguistic impacts, examine diachronic

stability of new terms, and assess how AI lexicons evolve in multilingual and
multicultural contexts.

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