Humaniser un texte IA: French Academic Register and Detectors

If a detector flags your French academic writing, the problem is usually not that you used AI. Formal French — long sentences, dense connectives, impersonal constructions — shares surface habits with machine output. Humaniser un texte IA means editing those habits out while keeping your argument, your terminology and your register intact.

Why does formal French academic register get misread?

Detectors look at two broad things: how predictable each word is given the words before it, and how much sentence length and structure vary across the passage. Academic French is predictable by design. It favours set phrases (il convient de souligner, force est de constater, dans la mesure où), it prefers the passive and the impersonal, and it builds long, evenly weighted sentences. That regularity is what a detector reads as machine-like.

Our own measurement, run on 2026-09-13 with the DeAIze detector (local ensemble, lite tier, 30% flag threshold), scored 23 hand-written DeAIze guides at a mean AI score of 21% and a median of 22%, range 0–46%, with 5 of 23 flagged. Twelve unedited model passages scored a mean of 33% and a median of 34%, range 4–46%. The two ranges overlap. Human academic prose can land inside the machine band, and machine output can land low. That overlap is the whole problem, and no single score settles authorship.

The same measurement counted AI-tell frequency: 4 per 100 words in the human sample versus 1 per 100 words in the unedited model output. Read that carefully. The human corpus carried more of the surface markers people associate with AI, not fewer. Frequency of tells is a weak signal on its own.

Which patterns look generated in French, and which are just academic style?

This distinction matters more than any score. Some habits are conventional and safe. Others are what actually pulls a passage toward the machine band.

Pattern	Conventional academic French?	Reads generated?	What to do
Il convient de souligner que	Yes, standard	Only in clusters	Keep one per section, not three
Long subordinate chains	Yes, expected	Yes when uniform	Break one sentence in three
Passive and impersonal	Yes, often required	Yes when unbroken	Convert some to active with a named subject
Triadic lists (X, Y et Z)	Yes, rhetorical	Yes when every paragraph has one	Vary to pairs or single items
En effet, ainsi, par ailleurs as openers	Yes	Yes when mechanical	Move some mid-sentence or cut
Nominalisations (la mise en évidence de)	Yes, dense register	Mildly	Unpack a few into verbs
Em dashes and colon-heavy rhythm	Less common in French	Yes	Use commas or restructure

A single instance of any row is fine. The signal comes from stacking. If three consecutive sentences open with a connective, use the same passive frame, and run to similar length, the passage reads as generated even when you wrote every word.

If you want to see which specific sentences carry the signal, DeAIze highlights AI probability sentence by sentence, so you can rewrite only the flagged lines and leave the rest of your argument untouched. Start free at /register — 200 words on signup, no credit card.

How do you edit without flattening your argument?

Work in passes, and never touch content and style in the same pass.

1. Lock the argument. Write out your claim, your evidence and your conclusion as a short outline. Everything you do next must preserve that outline exactly.
2. Lock the terminology. List the terms of art your field or your assignment requires — corpus, hypothèse, variable dépendante, whatever applies. These do not get paraphrased, ever. Rewriting a technical term to sound more human is how meaning dies.
3. Score first, edit second. Run the document through a detector that reports at sentence level. Note which sentences are flagged. Do not rewrite the whole text.
4. Vary construction, not content. For each flagged sentence, change how it is built: swap a passive for an active with a real subject, split a long subordinate chain, move a connective inside the clause, merge two short sentences into one longer one.
5. Re-score and compare. Keep the rewrite only where the meaning survived and the signal dropped. If a sentence scores lower but no longer says what you meant, revert it.

Here is an illustration, not a measured example.

Before: Il convient de souligner que la méthodologie employée, bien que largement répandue dans la littérature, présente certaines limites qu’il est nécessaire de prendre en compte afin de garantir la validité des résultats obtenus.

After: La méthodologie employée est courante dans la littérature. Elle présente toutefois des limites qui pèsent sur la validité des résultats.

The claim, the hedge and the terminology are identical. Only the construction changed. That is the whole move.

What are the risks of over-correcting?

This is the part most guides skip, and it is the part that costs people marks.

If you strip every connective, shorten every sentence and drop the impersonal voice, you no longer sound like an academic writer. You sound like a blog post. A marker or an editor will notice that before they notice any detector score, and you will have solved a detection problem by creating a register problem. In a thesis, a journal submission or a graded essay, register is assessed. Detection is not.

There is a second risk. Aggressive rewriting drifts meaning. A hedge becomes a claim, a qualified finding becomes a general one, a citation’s scope quietly widens. That is a worse failure than a flag, and it is harder to defend. If your institution has a policy on AI-assisted work, know what it actually says before you start editing — /blog/academic-integrity-policy-ai/ walks through where the line usually sits. And if you have already been flagged, /blog/false-positive-ai-detector-what-to-do/ covers what to prepare.

The honest framing: a detector score is a probability estimate, not proof of authorship. It cannot tell whether the ideas are yours. That judgement is yours to make and, if challenged, yours to document. If you did use AI to draft, the fix is to make the text genuinely yours — not to disguise someone else’s work.

How do you check the result still sounds like you?

Verification is a separate pass from editing, and it needs a different question. Not “does this score lower?” but “would I have written this?”

• Read it aloud. Machine-flat rhythm shows up immediately in speech. If every sentence takes the same breath, keep editing.
• Check your own tics. Most writers have habits — a favourite connector, a way of opening a paragraph. If they are gone, you over-corrected.
• Diff against your earlier work. Compare a paragraph from this draft with one from a text you wrote before AI tools existed. Same person? Then you are done.
• Re-score with a second tool. Different detectors weigh different signals, so agreement matters more than any single number. /blog/ai-detector-comparison/ shows what each one reports.
• Test the terminology. Read only the terms of art in sequence. If they still form a coherent chain, your argument is intact.

If you want the loop done for you, DeAIze scores the text, rewrites only the flagged sentences, keeps a rewrite only when semantic similarity says the meaning survived, then re-scores and reports before and after. On the example paragraph on the site, a 92% AI score becomes 4%. Across users the site reports an average AI-score reduction of 60%+, best case 71% — those are the product’s own predicted scores, not a third-party verdict. Pricing is pay-as-you-go, credits never expire: /pricing/.

What should you actually do next?

Pick the pass that matches your situation. If you wrote the text yourself and it flagged, do the variation pass — you are fixing a false positive, and you should change as little as possible. If you drafted with AI and are cleaning it up, do the full loop, but only after you have confirmed the ideas, data and conclusions are genuinely yours. Either way, lock your terminology first and your argument second, then edit. Never the reverse.

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Measurement note: figures in this article come from our own detector run on 2026-09-13 — n = 23 hand-written guides versus n = 12 unedited model outputs, median AI score 22% and 34% respectively. Method: /methodology/.