Language cannot be considered to be merely an aggregation of words put together in adherence to certain grammatical rules. Language, rather, becomes a system of meaning that comes into existence and gains relevance through the cultural context and history shared by people who speak the same language. One might consider idiomatic expressions to be the purest form of this idea, for words such as "under the weather", "burning the midnight oil", or "a blessing in disguise" take on meanings far removed from their literal definitions, which can only be fully comprehended through cultural understanding.
AI-based essay rewriting services have seen many improvements in fluidity, grammar correction, and academic writing style. However, idiomatic expressions, culturally embedded language, and other such elements pose challenges for these systems. Being pattern-based, AI-based rewriting services have learned to identify common idioms in languages such as English but often fail to handle figurative language, leading to inaccurate results. Therefore, it is important for students whose essays include any of these features to understand how AI handles them.
This article examines how AI essay-rewriting tools process idiomatic and culturally nuanced language, where they succeed and where they reliably fail, why these failure modes matter for writers from diverse linguistic backgrounds, and how to review AI-rewritten passages containing figurative or culturally specific content. Writers who want to explore a humanization tool with sensitivity to authentic voice and natural language variation can start with the BestHumanize humanizer, which is built to preserve the natural character of human writing rather than normalize it toward generic output.
Key Takeaways
Idioms are commonly misinterpreted by AI rewriting tools because these tools rely more on statistical models than on cultural significance.
Idioms in popular languages such as English are easily detectable, while figures of speech in other languages are likely to be misconstrued.
For students who rely on cultural idioms or humor in their writing, checking all figures of speech after rewriting becomes necessary.
Knowing how your preferred tool interprets figures of speech makes it easier for you to focus on what requires closer inspection.
It is always advisable to mark all idioms and cultural idioms before using any rewriting service, and then cross-check them with their original meanings.
How AI Rewriting Tools Process Figurative and Idiomatic Language
AI-based rewriting services use large language models trained on vast amounts of text data that have learned associations between terms. In these models, an understanding is formed of how certain word combinations co-occur and what functions they serve in their surrounding context. When dealing with widely used idiomatic expressions in standard English, this understanding is usually enough to preserve their idiomatic meaning. If an idiom like "hit the ground running" has been seen by a model thousands of times across various texts, there is a chance it will not be rewritten but left as is.
The problem occurs at the boundaries of statistical fluency. Uncommon idioms, regional sayings, culturally loaded terms, slang, and words that convey meanings quite different from their literal senses are the most often misinterpreted. When an AI rewrite tool is unsure whether a phrase is an idiom, it treats it based on its statistical rewriting tendencies, applying those tendencies to individual words. The consequence of such actions is a properly structured sentence that either does not convey its figurative sense anymore, replaces it with a literal one, or turns it into a generic saying altogether.
A 2025 study by Appen testing multiple LLMs on multilingual content containing figurative expressions found that puns and idioms were frequently translated literally, producing clunky or confusing output. Evaluators had to rewrite passages to restore cultural resonance and idiomatic accuracy. The study highlighted that this was not primarily a language-pair problem but a fundamental limitation in how LLMs handle figurative expression, one that applies to rewriting tasks within a single language as well as to translation between languages.
Common Failure Modes When AI Tools Rewrite Idiomatic Language
Literal Interpretation of Figurative Phrases
The most frequent cause of AI errors is literal translation of idioms. An author who uses the phrase “having an axe to grind” in relation to a colleague will notice that the AI program has changed the sentence in such a way that it now talks about literally sharpening axes, or else has changed the phrase into one that talks about general motives for personal gain without including the special shade of meaning inherent in the original phrase. The sentence is grammatically correct, but no longer carries the same meaning.
Research into AI writing patterns has confirmed that idiomatic misapplication is a reliable marker of AI-processed text. When AI tools insert idioms into technically correct positions but strip them of their figurative weight, or replace them with literal equivalents that read awkwardly in context, experienced readers recognize the gap between surface fluency and genuine linguistic competence. This matters both for the essay's communicative quality and for the writer's credibility with readers sensitive to authentic expression.
Cultural Reference Flattening
Another form of failure is cultural reference flattening, where an AI tool understands a phrase's cultural context and replaces it with a more generic one, eliminating the context altogether. A mention of an activity peculiar to a region, an event with special significance to a culture, or a metaphor grounded in a tradition familiar only to members of that culture can all become generic phrases understood by people from all cultures around the globe.
This loss of depth is especially significant in essays on cultural matters or personal essays, where cultural allusions play a key role in shaping the author's voice. An essay written by a Nigerian student who makes use of Yoruba proverbs in their natural state or an essay written by a South Asian student who relies on the philosophy of the region is rendered meaningless through the use of an AI rewriting service, as they try to make these cultural expressions conform to Western academic conventions.
Dialect and Register Erasure
The AI software for rewriting essays is mostly trained on edited academic English and therefore tends to treat any deviation from it as a mistake to be corrected, rather than as a valid alternative to be preserved. There are several varieties of English, each with its own grammar, idioms, and rhetorical patterns, which are considered part of linguistic variety rather than mistakes. However, when a piece of writing in one of those varieties is processed by an AI rewriting tool, it is systematically rewritten into edited academic English.
For many writers, this erasure is not a neutral quality improvement but a form of linguistic dispossession. The tool's default output reflects the dominant prestige variety of English, and its corrections implicitly characterize every deviation from that variety as a deficiency. Writers who choose to write in non-standard varieties for rhetorical, cultural, or political reasons will find that AI rewriting tools consistently work against those choices rather than supporting them.
Why AI Models Carry Built-In Cultural Blind Spots
The cultural limitations of AI essay rewriting tools are not accidental oversights but structural consequences of how these models are built. Large language models learn from text available on the internet and in digitized document collections. This text is not culturally representative. Research consistently shows that internet text is heavily skewed toward English, and within English, toward varieties associated with the United States, Western Europe, and educated professional contexts.
This training data imbalance has direct consequences for how AI rewriting tools handle culturally specific language. A model trained predominantly on standard American English will have seen millions of examples of American idioms, cultural references, and rhetorical conventions. It will have seen far fewer examples of equivalent features from other English-using communities, and fewer still from non-English cultural traditions that writers may draw on even when writing in English. The statistical competence the model has built up for the dominant variety does not transfer to the underrepresented ones.
The Brookings Institution's analysis of how language gaps constrain generative AI development identifies this imbalance in training data as a fundamental constraint that prompts that engineering cannot fully overcome. When the underlying training data does not represent a linguistic community, the model has not learned the patterns of that community's language, and its output will reflect that gap regardless of how specifically the user instructs it. Writers from underrepresented communities need to be especially aware of this limitation when using AI rewriting tools, because the tool's corrections may move their writing away from an accurate representation of their linguistic and cultural context.
The Homogenization Problem: When Rewriting Erases Cultural Diversity
One of the most significant consequences of AI rewriting tools' cultural limitations is homogenization: the tendency to produce output that sounds the same, regardless of the original writer's cultural background or voice. When AI tools normalize idiomatic expressions toward standard English, flatten cultural references, erase dialect features, and smooth regional rhetorical patterns into globally readable academic prose, they produce essays that could have been written by anyone. The writer's cultural specificity, which is often the most valuable and distinctive element of their work, is replaced by a generic academic voice.
This homogenization problem has implications beyond the individual writer. Academic and professional writing communities benefit from diverse voices, cultural perspectives, and ways of framing problems. When AI tools systematically convert all writing toward a single dominant standard, they participate in a process of cultural leveling that reduces the intellectual diversity of texts reaching readers. Writers who are aware of this tendency can resist it by reviewing AI-rewritten passages specifically for cultural flattening and restoring the culturally specific elements that the tool has removed.
Research published in Nature examining how language bias in AI affects underrepresented communities documents that AI models favor the language patterns of high-resource communities while systematically underperforming in lower-resource linguistic contexts. The researchers highlight that local initiatives are beginning to build alternative training datasets to address this imbalance, but these efforts have not yet reached mainstream essay-rewriting tools used by most students and writers. For now, the responsibility for preserving cultural nuance in AI-rewritten essays rests largely with the writer.
Specific Implications for ESL and Multilingual Writers
Writers who compose essays in English as a second or third language face particular challenges at the intersection of AI rewriting and idiomatic language. ESL writers often develop idiomatic competence gradually and unevenly, achieving a strong command of some expressions while remaining uncertain about others. When an ESL writer uses an idiom correctly, and the AI tool replaces it with a literal paraphrase, the result is actually less idiomatic than the original. The tool's correction has made the essay worse.
Conversely, ESL writers who use idioms incorrectly, applying them in contexts where they do not fit or misremembering their form, may find that the AI tool reinforces rather than corrects the error. Because the tool is making statistical judgments about plausible phrasing rather than semantic judgments about idiomatic accuracy, it may produce a sentence that reads fluently while still misusing the expression. The fluency masks the error in a way that the original awkward phrasing did not.
For multilingual writers in particular, the challenge of managing AI rewriting and AI detection simultaneously adds another layer of complexity. Detection tools flag ESL writing at elevated rates because its patterns resemble AI output, and AI rewriting tools that attempt to address this by standardizing language can worsen detection scores by removing the natural variation that distinguishes authentic human writing. This review addresses how these dynamics interact, focusing on ZeroGPT and how detection tools score writing. For questions specific to how BestHumanize handles multilingual and ESL content, the BestHumanize FAQ covers these scenarios in detail.
Where AI Essay Rewriters Actually Succeed with Figurative Language
It would be misleading to suggest that AI essay rewriting tools handle all figurative and cultural language poorly. For common, high-frequency idioms in standard English, current tools perform adequately. Expressions like "in the long run," "at face value," "on the fence," and "the tip of the iceberg" are sufficiently common in the models' training data that they are typically recognized as idiomatic units and either preserved or replaced with equivalent expressions rather than literally paraphrased.
Tools also tend to handle metaphors in academic writing reasonably well when those metaphors are widely used in academic discourse. The metaphors of "shedding light on" a topic, "building" an argument, or "navigating" a complex field are so embedded in the academic register that AI tools have absorbed these expressions as conventional features of the genre and reproduce them appropriately. The failure modes are most severe at the edges, with regional expressions, culturally and community-specific references, creative metaphors, humor, and non-standard varieties.
Writers who want to understand where their AI rewriting tool's idiomatic competence begins and ends can test it by submitting a passage containing a range of expressions, from common to uncommon, and carefully comparing the output against the original. Understanding how perplexity and natural variation relate to idiomatic expression is also relevant here; GPTZero's explanation of perplexity and burstiness helps writers understand why idioms, with their statistically unexpected word combinations, are both more likely to signal human writing and more likely to be normalized away by AI rewriting tools that optimize for predictability.
A Practical Review Protocol for Passages Containing Idioms and Cultural Language
Writers who use AI rewriting tools on essays containing idiomatic or culturally specific language should follow a targeted review protocol rather than treating the output as ready to use. The protocol has four components that together address the main categories of failure in idioms and cultural nuance.
Idiom identification pass: Before rewriting, annotate every idiomatic expression, figurative phrase, cultural reference, and dialect feature in the essay. After rewriting, verify that each annotated element has been either preserved accurately or replaced with an equivalent that carries the same figurative meaning. Any element that has been literally rephrased or culturally flattened requires manual restoration.
Meaning equivalence check: For each idiom that the AI tool has changed, ask whether the replacement carries the same meaning, the same connotation, and the same cultural register as the original. Replacing "a bitter pill to swallow" with "a difficult situation to accept" is semantically close but loses the sensory concreteness and embodied quality of the original expression. Whether that loss matters depends on the context, but the writer should make that judgment deliberately rather than defaulting to the substitution.
Cultural specificity audit: Review the rewritten essay for passages where culturally specific content has been replaced with generic language. If a reference to a specific cultural practice, historical event, or community tradition has been softened into a vague generalization, restore the specific original. Specificity is almost always more valuable than the accessibility an AI tool offers.
Voice consistency check: Read the rewritten essay aloud and assess whether it still sounds like you. This is the most subjective component of the review, but it is also the most important. If the AI tool has erased dialect features, regional phrasing, or rhetorical patterns that are part of your voice, those elements are worth restoring even if the rewritten version is technically more standard.
Writers who want more structured guidance on applying this protocol in a humanization workflow can find practical resources on the BestHumanize writing tips blog. For writers considering which plan best supports their review and revision needs, the BestHumanize plans and pricing page outlines the available options.
The Future of Cultural Awareness in AI Essay Rewriting Tools
The cultural limitations of current AI rewriting tools are not inherent to the technology but rather reflect where the field stands today. Researchers and developers are actively working to build more culturally representative training datasets, develop tools for evaluating cultural appropriateness alongside fluency, and design fine-tuning approaches that reduce the homogenizing tendencies of standard large language models.
Several regional initiatives are building AI models trained specifically on underrepresented languages and dialects, with the goal of producing tools that perform as well for speakers of those varieties as current tools do for speakers of standard academic English. For essay rewriting specifically, the integration of cultural competence signals into model evaluation frameworks is an emerging area of research, though one that has not yet been systematically incorporated into commercial essay rewriting products.
For writers using current tools, the most important implication of this trajectory is to treat AI-rewritten output as a starting draft rather than a finished product, particularly for passages where cultural and idiomatic accuracy matters. The BestHumanize team is available to discuss specific concerns about how humanization tools handle culturally nuanced language and what writers from diverse linguistic backgrounds should expect from the current generation of tools. The BestHumanize about page also describes how the platform's design philosophy balances standardization and the preservation of authentic voice.
Conclusion
AI essay rewriting tools handle idiomatic expressions and cultural nuances inconsistently. For common English idioms embedded in standard academic prose, current tools generally perform adequately. For regional expressions, culturally specific references, non-standard English varieties, and figurative language that depends on cultural familiarity for its meaning, these tools are considerably less reliable.
The failure modes, literal interpretation, cultural flattening, dialect erasure, and homogenization are not random but structural. They reflect the training data imbalance that currently shapes most large language models, the statistical nature of AI language processing, and the absence of genuine cultural understanding in systems that work through pattern recognition rather than semantic comprehension.
Writers who understand these limitations can work with AI rewriting tools productively while protecting the cultural and idiomatic dimensions of their work. The targeted review protocol described in this article, combined with a commitment to restoring what the tool has flattened, allows writers to benefit from AI assistance without surrendering the cultural specificity that makes their writing distinctively theirs.
Frequently Asked Questions
Do AI essay rewriters recognize common idioms?
Yes, for high-frequency idioms in standard English, most current AI essay rewriting tools recognize the phrase as an idiomatic unit and either preserve it or replace it with a close equivalent. The reliability of this recognition decreases significantly for less common idioms, regional expressions, and culturally specific figurative language. Writers should always verify after rewriting that idioms in their essay have been handled accurately, rather than literally paraphrased.
Why do AI rewriting tools flatten cultural references?
AI rewriting tools optimize for broad readability and conventional academic register, both of which tend to favor generic language over culturally specific references. When the tool encounters an expression or reference it does not confidently recognize as culturally significant, its default behavior is to replace it with something more standard and globally comprehensible. This produces fluent but culturally empty output. The tendency is a consequence of training on predominantly standard-English texts and of optimizing for surface fluency rather than cultural fidelity.
How does cultural bias in AI tools affect ESL writers?
Cultural bias in AI rewriting tools can affect ESL writers in multiple ways. The tools may correct idiom usage based on a misreading of the expression, reinforce idiom errors by producing fluent-sounding output that is still idiomatically wrong, erase linguistic features from the writer's native language that appear in their English writing, and homogenize their voice toward a dominant English variety that does not represent their cultural background. ESL writers should review AI-rewritten passages containing idiomatic or culturally specific language with particular care.
Can I use AI rewriting tools if my essay contains regional or non-standard English?
Yes, but with specific precautions. Before submitting the essay to an AI tool, annotate every passage that contains dialect features, regional expressions, or non-standard forms that you are using deliberately as rhetorical choices. After rewriting, compare those passages against the original and restore any features that the tool has normalized away. The tool will almost certainly have moved those passages toward standard edited English, and the restoration work will be the most important part of your post-rewriting review.
What should I check after an AI tool rewrites a passage containing idioms?
After any AI rewriting of a passage containing idioms, verify four things: that every idiom has been either preserved or replaced with a functionally equivalent expression; that no idiom has been literally paraphrased in a way that changes its meaning; that culturally specific references have not been replaced with generic language; and that the passage still sounds consistent with your voice and cultural perspective. If any of these checks fail, restore the original language manually before moving to the next section.
Disclaimer: This article is provided for informational and educational purposes only and does not constitute academic, linguistic, or professional advice. AI rewriting tools vary significantly in how they handle idiomatic and culturally specific language, and their performance characteristics may change as they are updated. Writers are responsible for reviewing all AI-assisted output before submission and for ensuring that their work accurately represents their intended meaning and cultural perspective. BestHumanize is committed to supporting authentic, voice-preserving writing assistance.