Can AI detectors tell a writer who got help from one who handed over the whole job, or only the extremes?

This explores whether AI-text detection can tell "I wrote this with AI help" apart from "AI wrote this for me." The most direct answer in the collection is a split verdict, and it comes from watching how text gets written, not from reading the finished text. When researchers looked at keystroke-level process data from writing and programming, delegation left a clear mark: AI contributions arrived in concentrated bursts that broke from the author's normal rhythm. Ordinary collaboration left no such mark. Lightly assisted work looked just like work with almost no assistance Can process data distinguish AI delegation from ordinary collaboration?. So detection catches the extreme case and goes quiet in the middle, which is where most real use happens.

Text-only detectors are good at a different question: did a machine produce this text? They do it in surprising ways. AI fiction can be spotted from its storytelling choices alone, such as how characters act and how time is ordered, even with every stylistic cue removed. Those choices resist "humanizing" because changing them means rewriting the story, not polishing sentences Can AI stories be detected without analyzing writing style?. Simple, readable features flag AI-written arguments with 99% accuracy, partly because LLMs show a habit of bending toward the prompt that humans don't Can simple linguistic features detect AI-written arguments?. Machines can also measure differences in word variety that human judges, including trained linguists, can't perceive. Newer models drift further from human text while becoming harder for people to notice Can humans detect AI text if machines can measure it?Can people reliably spot content made by AI?. But all of these detectors were tested on mostly-human versus mostly-AI text. None of them measures how much of the thinking a person kept.

The gap gets wider once people rewrite. Heavy revision makes AI-assisted messages converge stylistically, which erases authorship signals. The claim that the same rewrites also fool AI detectors, a "double erasure," has not been tested yet Do rewrites that hide authorship also fool AI detectors?. If it holds, the more someone actually collaborates with AI (revising, merging, reworking), the less any text-based detector can say about how the text came to be.

This suggests collaboration versus delegation may be the wrong job for detectors. The difference lives in the process, and some of the corpus approaches it from that side. Writers sharing an editor strongly preferred being able to see when, where, and how a co-author used AI prompts, because it helped them understand each other's thinking and check generated text Do writers want to see each other's AI prompts in shared editors?. On the writer's side, people feel more ownership of AI text when they have more control over it, while personalizing the model makes no difference Does user control over AI text shape feelings of ownership?. That makes "how much did the person steer?" the real dividing line, and it can be recorded as a person writes but is hard to recover afterward.

The collection is thin here. Only one study tackles the question directly, and it relies on process logs rather than text. Nothing in the corpus shows a text-only detector separating degrees of collaboration. The useful takeaway: detectors can tell you whether a machine wrote the words, but whether a person kept doing the thinking is visible mostly in how the writing happened, not in what ended up on the page.