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

要旨

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.

著者
Yufeng Hu
Tsinghua University, Beijing, China
Jinyi Zhang
RMIT University, Melbourne, Victoria, Australia
Zehuan Wang
City University of Hong Kong, Hong Kong, China
Chun Yu
Ministry of Education, Beijing, Beijing, China

会議: CHI 2026

ACM CHI Conference on Human Factors in Computing Systems

セッション: AI and Interactive Tools for the Arts

Auditorium
7 件の発表
2026-04-16 18:00:00
2026-04-16 19:30:00