AI Personalization Paradox: Reading Highlights for Personalized AI-Assisted Writing Increases Engagement but Undermines Autonomy and Ownership
Peinuan Qin, Chi-Lan Yang, Nattapat Boonprakong, Jingzhu Chen, Yugin Tan, Yi-Chieh Lee
Abstract
AI-assisted writing raises concerns about autonomy and ownership when benefiting writers. Personalization has been proposed as an effective solution while also risking writers' reliance on AI and behavior shifting. For better personalization design, existing studies rely on interaction and information solely within the writing phase; however, few studies have examined how reading behaviors can inform personalized writing. This study investigates the effects of integrating reading highlights for personalization on AI-assisted writing. A between-subjects study with 46 participants revealed that the personalization condition encouraged participants to produce more highlights. However, highlighting unexpectedly shifted from a sense-making strategy to an instrumental act of "feeding the AI," leading to significant reliance on AI and declines in writers' sense of autonomy, ownership, and self-credit. These findings indicate personalization risks in AI-assisted writing, emphasize the importance of personalization strategies, and provide design implications.
• Human-centered computing → Empirical studies in HCI; Human computer interaction (HCI).
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 24062670-3af7-4605-b68e-714ff20c9a96Builds on11
- AI-Augmented Brainwriting: Investigating the use of LLMs in group ideationOrit Shaer, Angelora Cooper, Osnat Mokryn, Andrew L. Kun et al.CHI 2024 · 120 citations
- Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language ModelsParamveer S. Dhillon, Somayeh Molaei, Jiaqi Li, Maximilian Golub et al.CHI 2024 · 102 citations
- AI Suggestions Homogenize Writing Toward Western Styles and Diminish Cultural NuancesDhruv Agarwal, Mor Naaman, Aditya VashisthaCHI 2025 · 93 citations
- Optimization Methods for Personalizing Large Language Models through Retrieval AugmentationAlireza Salemi, Surya Kallumadi, Hamed ZamaniSIGIR 2024 · 52 citations
- 'It was 80% me, 20% AI': Seeking Authenticity in Co-Writing with Large Language ModelsAngel Hsing-Chi Hwang, Q. Vera Liao, Su Lin Blodgett, Alexandra Olteanu et al.CSCW 2025 · 36 citations
Related papers
- Co-Writing with AI, on Human Terms: Aligning Research with User Demands Across the Writing ProcessMohi Reza, Jeb Thomas-Mitchell, Peter Dushniku, Nathan Laundry et al.CSCW 2025 · 29 citations
- The Role of Inclusion, Control, and Ownership in Workplace AI-Mediated CommunicationKowe Kadoma, Marianne Aubin Le Quéré, Xiyu Jenny Fu, Christin Munsch et al.CHI 2024 · 21 citations
- Investigating How Computer Science Researchers Design Their Co-Writing Experiences With AIAlberto Monge Roffarello, Tommaso Calò, Luca Scibetta, Luigi De RussisCHI 2025 · 3 citations
- Reactive Writers: How Co-Writing with AI Changes How We Engage with IdeasAdvait Bhat, Marianne Aubin Le Quéré, Mor Naaman, Maurice JakeschCHI 2026 · 5 citations
- Designing and Evaluating AI Margin Notes in Document Reader SoftwareNikhita Joshi, Daniel VogelCHI 2026 · 1 citation
