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

📅 2026-01-15
📈 Citations: 1
✨ Influential: 0
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🤖 AI Summary
This study addresses the tension between efficiency gains from AI writing assistants and the potential erosion of authors’ psychological ownership over their text. To mitigate this trade-off, the work proposes five design patterns aimed at preserving authorial identity: on-demand activation, micro-suggestions, voice anchoring, audience scaffolding, and decision-point provenance. Through an online controlled experiment, two strategies—role-based coaching and style personalization—were evaluated. Results indicate that style personalization significantly enhances psychological ownership (+0.43) and increases AI content adoption by 5%, whereas role-based coaching fails to counteract the decline in ownership. Notably, cognitive load is reduced without compromising text quality. This research advances a novel paradigm for collaborative writing systems that effectively balances productivity with authorial agency and identity.

Technology Category

Humans and AI: Human-AI Collaboration / Human-AI TeamingCognitive Modeling & Cognitive Systems: Computational CreativityMultiagent Systems: Mechanism Design

Application Category

Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAIUser Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systemsResponsible Web: Human-perceived consequences of algorithmic deployment on the web
📝 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.
Problem

Research questions and friction points this paper is trying to address.

authorship
AI-assisted writing
psychological ownership
writing assistants
text ownership
Innovation

Methods, ideas, or system contributions that make the work stand out.

authorship preservation
AI-assisted writing
psychological ownership
style personalization
design patterns
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Bohan Zhang
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