Generalization of editing effects to AI-generated text

Determine whether the effects of professional linguistic editing observed for human-authored academic manuscripts generalize to AI-generated text and whether professional editing affects human-written and AI-generated texts in systematically different ways.

Background

The study uses paired human-authored manuscripts and their professionally edited versions to isolate the relationship between linguistic refinement and AI-detector outputs while controlling for topic, content, and authorship. This design demonstrates that editing can alter detector scores for human-written text, but it does not establish whether the same alterations occur when AI-generated text undergoes comparable human editing.

The unresolved issue is important because a detector may respond differently to editing depending on the text’s underlying origin. Matched human- and AI-generated texts subjected to controlled levels and types of human editing would be needed to determine whether professional editing interacts systematically with authorship origin.