A First-Language-by-First-Language Map of Which English Errors Lower vs Raise AI Scores

You have probably heard that AI detectors are unfair to non-native English writers. That is true, but it is blunt. The more useful question is which specific habits, carried over from which first language, push a detector’s score up and which push it down. Think of what follows as an L1 errors AI detection score map: a rough guide to how patterns from a few language families interact with the perplexity and sentence rhythm that detectors actually measure.

The short version is that idiosyncratic errors a language model would rarely make tend to raise your perplexity and read as more human, while smoothing habits — safe vocabulary, memorized phrasing, even sentence length — lower perplexity and read as more AI, and both effects are tendencies across many writers, not guarantees for any single essay.

Key takeaways

• A detector scores the statistics of your text, not your nationality. First languages matter only because they nudge you toward predictable or surprising patterns.
• The core principle: idiosyncrasy raises perplexity and reads human; smoothing lowers it and reads AI.
• East Asian, Romance, and Slavic writers each carry distinct habits, and within each group some habits raise the score while others lower it.
• None of this is a rule. It is a pile of tendencies with heavy overlap and plenty of exceptions.
• The takeaway is never to add mistakes on purpose. It is to write with specific detail and varied sentences, which raises perplexity honestly.
• Detectors misfire on non-native writing often enough that you should keep your drafts and notes as evidence.

How to read this L1 errors AI detection score map

Before the language groups, one idea holds the whole thing together. Detectors mostly estimate two numbers. Perplexity is how surprised a model is by your next word given the words before it. Burstiness is how much your sentence length and rhythm vary. You can read the full breakdown in our piece on perplexity and burstiness, but the part that matters here is simple.

A language model produces text that is very predictable word to word. So anything that makes your writing *more* predictable pulls the score toward AI, and anything that makes it *less* predictable pulls it toward human. That gives you two opposite forces inside almost every non-native draft.

The first force is idiosyncrasy. When your first language pushes you to place an article, an aspect marker, or a preposition somewhere a model would not, you produce a small surprise. The model did not expect that move, perplexity ticks up, and the detector reads it as more human. The second force is smoothing. When you reach for a phrase bank, a memorized connective, or the safest possible word, you produce exactly what a model would produce, perplexity drops, and the score climbs toward AI.

Here is the honest, uncomfortable catch. The same writer often does both in the same paragraph. The exam-prep sentence smooths; the article slip surprises. So no first language maps to a single score. What follows is a description of tendencies, not a lookup table.

East Asian first languages (Chinese, Japanese, Korean)

Mandarin has no articles, and Japanese and Korean handle time through aspect and context rather than a rigid tense system. Carried into English, that produces article choices and tense patterns that a model would rarely generate: a dropped *the*, an extra *a*, a present tense where a native writer expects a perfect. Each of those is a genuine surprise. On the idiosyncrasy side of the map, these habits tend to nudge perplexity up and can read as more human.

The pull in the other direction is strong, though. A lot of East Asian academic English is learned through high-stakes exam preparation, where students memorize connective lists, template openers, and safe “band-raising” vocabulary. That is smoothing in its purest form. A paragraph built from *moreover*, *it is widely acknowledged that*, and *plays a significant role* is close to what a model outputs, so it drags the score toward AI even though a human wrote every word.

The result is a draft that a detector can pull in both directions at once. The takeaway is not to keep the article slips. It is to notice how much of the *rest* of the writing runs on memorized rails, and to replace that filler with specific claims from your own reading.

Romance first languages (Spanish, French, Italian, Portuguese)

Romance writers tend to bring two habits worth separating. The first is long, heavily subordinated sentences, which is normal and often elegant in the source language. The second is a heavy load of Latinate cognates: *utilize*, *facilitate*, *demonstrate*, *significant*.

Those cognates are a quiet problem for the score. High-register Latinate vocabulary happens to be exactly the register language models favor, so leaning on it is a form of smoothing that lowers perplexity. Long sentences are more ambiguous. If your sentences are long *and* uniform in length, burstiness stays low and the writing reads flat. If the lengths vary — a couple of long ones, then a short one — that same tendency can actually help.

The idiosyncratic side shows up as false friends and preposition patterns: *assist to a meeting*, *depend of*, *actual* meaning current. A model rarely makes those exact substitutions, so they behave like the article slips above and nudge perplexity up. Again, the point is not to preserve the errors. It is to trade some of the safe Latinate vocabulary for plainer, more specific words, and to let your sentence lengths vary on purpose.

Slavic first languages (Russian, Polish, Ukrainian, Czech)

Most Slavic languages have no articles at all, so systematic article omission is one of the most distinctive non-native patterns in English. Dropping *the* and *a* where English wants them is something a model almost never does, because a model has learned those articles as near-certain. That makes article omission a strong idiosyncrasy signal: it can raise perplexity noticeably and read as human.

Slavic word order is also more flexible, driven by emphasis and case rather than fixed position. Carried into English, that can put a word in an unusual slot, another small surprise the model did not predict. On the map, both of these sit on the perplexity-raising side.

But the same caution applies. Slavic writers who learned English through formal grammar drills often produce very correct, very even prose once the articles are in place, and that correctness is smoothing. So the score depends less on the first language than on how much memorized, textbook-flat phrasing sits around those distinctive quirks.

The general principle, and why it is not a strategy

Put the three groups together and one pattern repeats. Idiosyncrasy raises perplexity and reads human. Smoothing lowers it and reads AI. Your first language sets which idiosyncrasies you are prone to and which smoothing habits you were taught, but the mechanism is the same for everyone.

It is tempting to read that as advice to keep your errors, or worse, to add some. Do not. Graders still mark mistakes, so manufactured errors cost you real marks. Detectors are noisy, so fake errors do not reliably move the score anyway. And OpenAI retired its own AI text classifier in 2023 for low accuracy, which should tell you how shaky these tools are to game in the first place. The idiosyncrasy effect is real but unreliable, and it is a description of what happens, not a plan you should follow.

What to actually do

Aim the effort at honest sources of surprise instead:

1. Cut the phrase-bank filler first. Memorized connectives and safe intensifiers are the biggest smoothing culprit, and they are the same words in every language group.
2. Vary sentence length on purpose. Break one long sentence in two, fold two short ones together, and land a point with a blunt five-word sentence.
3. Add detail only you could write. A specific study, figure, or example from your own reading raises perplexity for a real reason and reads as more competent to a grader.
4. Keep your evidence. Save drafts, notes, and version history. A 2023 study by Liang and colleagues found several detectors flagged non-native English far more often than native English, so a false positive is a risk you should be ready to contest.

All four make the writing better on its own terms. The lower detector score is a side effect, not the goal.

Frequently Asked Questions

Do writers with different first languages really get different AI detection scores? On average, yes, but not in a clean, rule-like way. A detector reacts to the statistics of your text, not to your passport. First languages nudge you toward certain habits: some produce prose that a model would also produce, which reads as AI, and some produce quirks a model rarely generates, which read as human. Those are tendencies across many writers, not a score you can predict for any single essay.

Should I add mistakes to my English so it reads more human? No. Deliberately inserting errors is bad writing advice and it usually backfires. Graders still mark the errors, and detectors are noisy enough that fake mistakes do not reliably move the score. The honest goal is to write in your own voice with concrete, specific detail, which raises perplexity for real reasons instead of manufactured ones.

Which first-language habits tend to lower perplexity and read as AI? Smoothing habits. When learners lean on phrase banks, safe high-frequency vocabulary, memorized connectives, and very even sentence length, the text becomes predictable word to word. That low-variation style overlaps with what language models produce, so a detector can rate it high-probability AI even though every word is yours.

Which habits tend to raise perplexity and read as human? Idiosyncratic ones. Article choices, aspect and tense patterns, or word order carried over from your first language often surprise a model, because a model rarely makes those exact moves. That surprise shows up as higher perplexity, which detectors read as more human. It is a genuine signal, but an unreliable one, and it is not a reason to write worse on purpose.

If detectors are biased against non-native writers, what should I do? Keep evidence of your process and write specifically. A 2023 study by Liang and colleagues found several detectors flagged non-native English far more often than native English, so a false positive is a real risk you should be ready to contest. Save drafts, notes, and version history. Then focus on concrete detail and varied sentences, which improve the writing and reduce predictability at the same time.

The map is a description, not a shortcut

Your first language leaves fingerprints on your English, and some of those fingerprints happen to look human to a detector while others look like a machine. That is interesting, and it explains why “just be a non-native writer” is not a reliable defense in either direction. What holds up is the boring advice: write with specific detail, let your sentences breathe, and keep proof of how you worked.

When you want a second opinion before you submit, you can check a draft and humanize it to see which sentences a detector still finds predictable, then fix only those. If you are weighing how often you will use it, see which plan fits, or browse more guides on writing and detection to keep sharpening the habit.