Plotania: Exploring Transparency Trade-offs in AI Co-Writing Through Virtual Readers and Transparent Attribution

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Authors: Yufeng Hu, Jinyi Zhang, Zehuan Wang, Chun Yu
Cited by: 1
References: 63

Abstract

Current AI writing tools aim to enhance authorial capacity yet often diminish authorial control and lack timely audience feedback. Through a formative study with fiction authors (N=10), we uncovered two critical tensions in human–AI co-writing: balancing AI scaffolding with authorial ownership, and the absence of contextual audience perspectives that shape storytelling during drafting. Guided by these insights, we designed Plotania, a co-writing system that combines proactive virtual readers offering real-time audience reactions with transparent attribution layers. A controlled study (N=20) revealed complex and counterintuitive effects: virtual reader feedback increased audience awareness but decreased perceived creative agency, transforming individual authorship into collaborative performance. Transparent attribution raised awareness of AI contributions but triggered identity anxiety and reduced AI usage. These findings reveal fundamental trade-offs in transparency design. We contribute design principles for “agency-preserving transparency” that balance information provision with creative empowerment, informing future transparency design in human-AI creative collaboration.

Fields of Study & Subjects

Artificial Intelligence in Healthcare and Education Ethics and Social Impacts of AI AI in Service Interactions

Publication details

Journal / source
Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems
Publisher
ACM
Pages
1–23
Publication date
13 April 2026
Language
EN