Can AI translation be flagged like AI writing in universities?

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AI translation can be flagged as high AI use, but it is not as reliably proven as AI writing.

Universities can sometimes flag AI translation in the same broad way they flag AI writing, because AI-detection tools may record that a submission shows “high AI use” when students use AI platforms for translation. However, detection is often difficult to interpret: it can be hard to distinguish “co-pilot writing the student’s assignment” from “Gemini translating the student’s assignment,” and software can also mislabel human-written text. The result is that flags do not necessarily prove that a student used a specific chatbot to produce the sentences.
AI detection software can mark translation as “high AI use”
AI detection software is described as not cheap, with detection rates and costs rising, which affects how consistently institutions can enforce rules. When students use AI platforms to translate, that usage can “show up” as high AI use, even if the student is not generating original writing in the same way as a chatbot-assisted draft. This means translation-related submissions may be flagged by these systems rather than treated as neutral automatically.
It is hard to separate translation from writing
The key difficulty is distinguishing “co-pilot writing the student’s assignment” (described as bad) from “Gemini translating the student’s assignment” (described as neutral). Even when detection flags “inappropriate” AI use, the broader question remains whether the student actually used AI to write the particular sentence or paragraph, or whether the software made an error. In practice, the same signal can cover different kinds of AI assistance, including translation.
Flags can be wrong, including on well-known human text
The detection problem is illustrated by running the Gettysburg Address through multiple “anti-AI” programs, where portions may be flagged. This example is used to show that AI detection can produce incorrect labels even when the underlying text is human-authored. So, a university system marking a submission can reflect misclassification rather than confirmed AI authorship.
Unlike plagiarism, detection often cannot prove which chatbot was used
A major limitation is that AI detection is described as often unable to be “proven” in the way plagiarism detection can be. With plagiarism tools, similar sources and matching paragraphs can be listed, but with chatbot-based writing or translation, it is “almost impossible” to prove that a student used a specific chatbot (such as Deepseek) to construct a paragraph. Verification is described as relying mainly on interviews with the student, which works best for post-graduate cases but becomes impractical when student numbers reach the hundreds.

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Can AI translation be flagged like AI writing in universities?

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