Do Deliberate Typos Lower Your AI Detection Score?

It depends on the detector, and the research points both ways. In one large benchmark, adding common misspellings barely changed what four commercial detectors caught. In another study, a handful of random typos in a short text pushed most of the research detectors it tested below chance. Turnitin was in neither study, and we have found nothing Turnitin has published on the question. What does not depend on the detector is the cost: the errors stay in your paper, and the person grading it reads every one.

Some students say it worked for them. One, quoted below, says their score dropped, and in the very next sentence says what it cost them. That trade is what this page is about.

Both studies were published at ACL 2024, the annual conference of the Association for Computational Linguistics.

RAID is a benchmark its authors describe as the largest of its kind: 509,014 AI-generated texts from 11 language models across 8 kinds of writing, run through 12 detectors before and after 11 kinds of deliberate interference. Its misspelling attack was built to look like real slips. The researchers compiled a dictionary of common misspellings from a Wikipedia list, changed only words that appear in it, "to minimize suspicion from human readers", and applied it to 20% of the eligible words.

The researchers set each detector's threshold so that it wrongly flagged 5% of human-written texts, which is not necessarily how the products are set up by default. At that threshold, this is how much of the AI-written text the four commercial detectors still caught:

All twelve detectors lost some ground. The two that lost most, 15.3 and 17.2 points, were open-source models trained on output from GPT-2, a language model released in 2019. The full table is Table 16 in the paper's appendix.

Stumbling Blocks, a second study at the same conference, used keystroke-style typos (letters inserted, dropped, substituted or transposed) rather than a dictionary of common misspellings, on texts averaging about 110 tokens, and tested open-source and research detectors. Its authors report that a few character-level edits per text were enough to "degrade the performance of most detectors to be worse than random". A few detectors held up well. No commercial detector was tested.

The two results differ because the detectors, the kind of typo and the length of text all differ. Neither tells you what will happen with the detector your school uses, and two gaps apply to both:

• Turnitin was not tested in either. Neither paper mentions it.
• Neither reports what typos do to human writing. RAID's public data includes human-written texts with the same misspellings added, but the paper reports detector results only for AI-generated text. If the essay being flagged is one you wrote yourself, these numbers do not describe your situation.

In RAID, the interference that moved scores most on average across the twelve detectors was not a typo at all. It was swapping letters for identical-looking characters from other alphabets.

Turnitin's Similarity Report has a separate Flags tab for exactly this. It reverses the swap before comparing your text and marks the "replaced characters" for the reviewer. We went through that documentation in the integrity flags on a Similarity Report. A tool that promises to beat detectors this way is likely to leave that flag on the report your instructor opens.

A thread on r/Professors in March 2026, with nearly 700 upvotes and more than 200 comments, put both sides on one page.

A student in it described what they changed after being flagged on paper after paper: writing "like i'm sending an email to a friend", varying sentence length, and choosing "to leave a typo here and there and suddenly my ai score drops." Then: "i absolutely hate the papers i'm producing now." In a thread on the Ateneo de Manila University subreddit in August 2026, another student wrote: "i sometimes have to make my work sound stupid (sometimes i add 1 grammatical error) just so that i wont get flagged". We cannot check those scores. They are reports, not measurements.

Instructors in the same r/Professors thread described the other side of the trade. One wrote: "Inserting mistakes would just result in a lower mark obviously." Another described a student whose misspellings "were not normal mistakes a native or fluent speaker would ever make", giving the example of "sinacism" written for "cynicism".

So a score may move, and the paper still gets worse. Made-up errors cost marks wherever spelling and grammar are graded, and errors that do not look like real slips make the person grading you suspicious. Look at your own rubric: if it has a line for spelling, grammar or mechanics, every error you add is counted there.

Not every instructor agrees about grading spelling at all. In the same thread one pushed back on docking marks for spelling and grammar, because it falls hardest on students with dyslexia, non-native writers and others. That was an argument about fairness in grading, not advice to add errors.

The milder version of the same idea is to write below your level: drop the words you like, avoid dashes, keep every sentence short. In the Ateneo de Manila thread one student wrote: "Not 'dumbed' down per se but I stopped using words na I typically use (ex: delve) para lang di ma-flag", the last part meaning "just so it won't get flagged". Another wrote: "no more em dashes for me". An instructor in the r/Professors thread said that "Many students in the past year have told me they've dumbed-down their writing to avoid AI-accusations."

The cost is the same as with typos: marks you can count, against an effect on the score that nobody can promise. The advice students are given about style also points two ways at once, which we set out in should you change how you write to avoid being flagged.

1. Keep your drafts and version history. If a score is ever questioned, the history of how the document grew answers the question in a way that no change to the final text can.
2. Read which passages were flagged, not just the percentage. A detector report highlights specific sentences. Those are the ones to look at.
3. Change flagged passages for meaning, not for noise. If a highlighted sentence is generic, make it say something only your paper says: the specific result, the specific source, your own reasoning. That is a better sentence whatever the detector thinks of it.
4. Talk to your instructor early. A conversation before the deadline, with your drafts in hand, goes better than an appeal after it.

If you have a Turnitin or iThenticate report and the flagged passages need rewriting, that is what HumanPen's humanize option does. Import the Turnitin or iThenticate report and rewrite only the passages it flags, returned as the same editable file. It does not add spelling or grammar errors as a way of changing a detection result, as our comparison of AI humanizers sets out. Read what comes back before you submit it. The passages it rewrites still carry your name.

Will Turnitin catch deliberate typos? We have found no public test of Turnitin on this, and nothing about it in the Turnitin AI detection guides and blog posts we checked. What is documented is that the Similarity Report flags replaced characters, which is a different trick.

Does fixing my spelling with a grammar checker raise my AI score? That is a separate question from adding errors. Turnitin says its detector is not tuned to target Grammarly's spelling, grammar and punctuation fixes, and that in its tests they were not flagged "in most cases"; does Turnitin detect Grammarly goes through what it said, including the generative features it treats differently.

Did the research test typos on human writing? Not in its reported results. RAID's public data includes human-written texts with misspellings added, but the paper reports scores only for AI-generated text, and Stumbling Blocks tested AI-generated text too. For an essay you wrote yourself, the cost in marks is the part you can be sure of.

• Liam Dugan et al., RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors, Proceedings of ACL 2024, read 27 September 2026 (Table 16; Appendix E.4).
• Yichen Wang et al., Stumbling Blocks: Stress Testing the Robustness of Machine-Generated Text Detectors Under Attacks, Proceedings of ACL 2024, read 27 September 2026.
• r/Professors, Students are deliberately writing worse to avoid AI detection flags, posted 3 March 2026, read 27 September 2026.
• r/ADMU, have you ever "dumbed down" your assignment to avoid getting flagged by an AI detector?, posted 28 August 2026, read 27 September 2026.