Who Owns the Text? Design Patterns for Preserving Authorship in AI-Assisted Writing

TL;DR

This study examines how AI-assisted writing tools impact authors' sense of ownership, finding that certain design patterns can help preserve authorship while maintaining writing quality and reducing cognitive load.

Contribution

The paper introduces five design patterns for AI writing tools that help maintain authorship identity and explores their effects through an empirical online study.

Findings

Ownership declines with AI suggestions but can be partially restored by style personalization.

Style personalization increases AI integration and helps preserve authorship.

Design patterns like on-demand suggestions and voice anchoring support authorship preservation.

Abstract

AI writing assistants can reduce effort and improve fluency, but they may also weaken writers' sense of authorship. We study this tension with an ownership-aware co-writing editor that offers on-demand, sentence-level suggestions and tests two common design choices: persona-based coaching and style personalization. In an online study (N=176), participants completed three professional writing tasks: an email without AI help, a proposal with generic AI suggestions, and a cover letter with persona-based coaching, while half received suggestions tailored to a brief sample of their prior writing. Across the two AI-assisted tasks, psychological ownership dropped relative to unassisted writing (about 0.85-1.0 points on a 7-point scale), even as cognitive load decreased (about 0.9 points) and quality ratings stayed broadly similar overall. Persona coaching did not prevent the ownership decline. Style personalization partially restored ownership (about +0.43) and increased AI incorporation in text (+5 percentage points). We distill five design patterns: on-demand initiation, micro-suggestions, voice anchoring, audience scaffolds, and point-of-decision provenance, to guide authorship-preserving writing tools.