Can you tell which paragraphs in a document a chatbot wrote and which a person did, or only software can?

This explores whether a person reading a document can spot which passages an AI wrote and which a human wrote, and whether anything else can do it instead. The corpus's answer is mostly no by eye, yes by machine. No note tests paragraph-level marking inside a single mixed document, so that part is inferred from adjacent findings.

On readers, the evidence is bleak. AI text differs measurably from human text on six vocabulary-diversity dimensions, and newer models diverge further. Yet human judges, including trained linguists, can't reliably tell the difference (Can humans detect AI text if machines can measure it?, Can human judges detect measurable differences in AI text?). Reading passively, which is how most of us read most documents, is worse. In a displaced Turing test, people and AI judges reading transcripts scored below chance. Only an interrogator who could ask follow-up questions kept a marginal edge (Can humans detect AI by passively reading its text?). A document can't be questioned, so that edge is unavailable.

Readers do pick up something, though, without being able to name it. AI-written posts read as aloof because they don't make the internal appeal to the reader's attention that human writing makes (Does AI writing lack the internal appeal to attention that humans use?). AI prose is also grammatically fluent but argumentatively inert, because it avoids the evaluative stance-taking that human writers do (Why does AI writing sound generic despite being grammatically correct?). The effect shows up in how readers judge the author. In a study with 11,091 readers, AI assistance shifted their impression of the writer on all 29 measured dimensions, toward more extreme, confident and agreeable (Does AI writing assistance change how readers perceive the writer?). So the AI's fingerprints reach the reader's impression even when they don't register as AI.

Machines do much better. Simple, interpretable linguistic features caught LLM-written arguments with 99% accuracy (Can simple linguistic features detect AI-written arguments?). A detector looking only at narrative structure, such as character agency and chronological order, separated AI fiction from human fiction with 93.2% accuracy, and it held up with style cues stripped out. These structural choices can't be fixed by polishing the wording, only by rewriting (Can AI stories be detected without analyzing writing style?). One theory of why: artificial text lacks embodied authorship and a real conversational counterpart, so it is structurally missing things human text has, and AI hotel reviews are flagged at 80%+ accuracy because they are false about personal experience (Does AI-generated text lose core properties of human writing?).

This matters for mixed documents because the human touch rarely erases these signals. Writers edited AI-generated paragraphs only 23% of the time, and the edits kept about 96% of the original (Do writers actually edit AI-generated text before publishing?). Writers also chose the AI version of their own paragraph 63% of the time, even though it distorted their stance (Do writers actually prefer AI-edited versions of their own text?). So AI passages tend to reach readers nearly intact, and readers can't flag them. The corpus points to provenance as the practical fix: one system binds every number, quote and asset to its source, making traceability, not fluency, the trust test (Can source traceability make AI writing trustworthy?). Since perception fails, a document would have to carry its own record of where each part came from.