Can AI Writing Tools Make Human Expression More Similar?

Large language models can help an individual generate more ideas or polish a draft, yet widespread reliance on the same models may make the language and perspectives produced by different people more similar. Researchers describe this as homogenization.

A 2026 review in Trends in Cognitive Sciences brings together evidence from linguistics, cognitive science, and computer science. It argues that LLMs can reflect dominant patterns in their training data and reinforce convergence when many users depend on similar systems. The paper is a review and argument based on existing evidence—not a single experiment proving that chatbot use makes all people think the same way.

What “homogenization” means here

Homogenization does not mean every user produces identical text. It refers to a reduction in variation across outputs: writing may converge toward similar vocabulary, sentence patterns, tones, arguments, or assumptions.

An AI system predicts likely continuations from patterns learned during training. When prompted to make text “professional,” “clear,” or “better,” it may repeatedly favor broadly accepted forms. Those revisions can improve readability while also removing regional language, unusual phrasing, cultural context, or an author's distinctive rhythm.

Three levels of possible influence

• Expression: different writers receive similar wording and style suggestions.
• Ideas: users begin with overlapping lists of examples, arguments, or solutions proposed by the model.
• Social norms: AI-polished language becomes expected in schools or workplaces, encouraging even non-users to imitate it.

Evidence is strongest for observable outputs such as text and generated ideas. Claims about long-term changes in a person's underlying cognition are harder to establish and require careful longitudinal research.

Why diversity matters

Cognitive and linguistic diversity can give a group multiple ways to frame a problem, detect assumptions, and propose solutions. If everyone begins from the same generated outline, a team may produce many polished versions of one approach while missing alternatives that would have emerged from independent work.

Language also carries information about identity, community, context, and experience. Standardizing it can make communication easier in some settings, but it can also erase distinctions that matter for cultural preservation, research, assessment, and personalization.

What empirical studies have found

Separate research has reported homogenizing effects in particular tasks. A study of AI-assisted creative ideation found that individuals could produce more detailed ideas while the ideas across different users became less distinct. Another line of work examined how LLM rewriting can preserve core content while changing stylistic markers and reducing linguistic diversity.

These findings are task- and setup-dependent. Results can vary with the model, prompt, language, user, editing behavior, and method used to measure similarity. They do not justify the claim that every use of an LLM reduces creativity.

The related paper “The Shrinking Landscape of Linguistic Diversity in the Age of Large Language Models” reports experimental and observational studies of style change in AI-assisted writing. As always, readers should inspect the methods and limitations, not only the headline.

How the effect can happen

1. Training-data concentration: widely represented styles and viewpoints can dominate model responses.
2. Optimization for acceptability: systems often favor answers likely to be judged clear, helpful, and safe by many users.
3. Default prompts: millions of people ask for “professional” or “engaging” writing without defining their own voice.
4. Anchoring: the first generated answer shapes what the user considers, even when they later edit it.
5. Repeated feedback loops: AI-generated language enters public text and may influence future writing and training data.
6. Institutional pressure: standardized AI-polished output becomes a norm that writers feel expected to match.

AI can broaden ideas as well as narrow them

The effect is not one-directional. A user can ask an LLM to identify missing perspectives, challenge an assumption, translate across languages, or generate deliberately different approaches. Someone with limited access to collaborators may encounter options they would not have considered alone.

The key distinction is between using AI as the first and only source of ideas and using it as one tool within a process that preserves independent thought, lived experience, and disagreement.

How to preserve your own voice

• Write a rough draft or position before asking AI for help.
• Ask for diagnosis—“Where is this unclear?”—before requesting a complete rewrite.
• Tell the model which phrases, dialect, tone, or structure must remain.
• Compare several genuinely different approaches rather than accepting the first answer.
• Restore concrete examples from your own experience.
• Read the final text aloud and replace language you would not naturally use.
• Keep earlier drafts so you can see what the tool changed.

How teams can protect diversity of thought

1. Have people form initial judgments independently before using a shared AI assistant.
2. Collect proposals before showing participants a model-generated summary.
3. Assign different people to challenge assumptions, represent users, or explore alternatives.
4. Vary evidence sources, not merely prompts to the same model.
5. Record minority views instead of asking AI to collapse every disagreement into consensus.
6. Evaluate originality and reasoning, not only polish and grammatical conformity.

Questions the research still needs to answer

• Do stylistic effects persist after people stop using a writing assistant?
• How do outcomes differ across languages, cultures, education levels, and accessibility needs?
• Which model or interface choices increase or reduce convergence?
• Can diverse training, personalization, and user control preserve expression without reducing usefulness?
• How should similarity be measured without treating every shared convention as harmful?

A careful conclusion

The evidence supports a real concern: when many people use similar systems with similar prompts, their outputs can converge. It does not establish that AI has already standardized human thought or that using a chatbot inevitably reduces creativity.

The practical response is not to avoid AI entirely. It is to decide where originality matters, think before generating, use multiple sources and perspectives, and treat fluent model output as material to examine rather than a default voice to adopt.