Reactive Writers: How Co-Writing with AI Changes How We Engage with Ideas
Advait Bhat, Marianne Aubin Le Quéré, Mor Naaman, Maurice Jakesch
Abstract
Emerging experimental evidence shows that writing with AI assistance can change both the views people express in writing and the opinions they hold afterwards. Yet, we lack substantive understanding of procedural and behavioral changes in co-writing with AI that underlie the observed opinion-shaping power of AI writing tools. We conducted a mixed-methods study, combining retrospective interviews with 19 participants about their AI co-writing experience with a quantitative analysis tracing engagement with ideas and opinions in 1,291 AI co-writing sessions. Our analysis shows that engaging with the AI's suggestions-reading them and deciding whether to accept them-becomes a central activity in the writing process, taking away from more traditional processes of ideation and language generation. As writers often do not complete their own ideation before engaging with suggestions, the suggested ideas and opinions seeded directions that writers then elaborated on. At the same time, writers did not notice the AI's influence and felt in full control of their writing, as they-in principle-could always edit the final text. We term this shift Reactive Writing: an evaluationfirst, suggestion-led writing practice that departs substantially from conventional composing in the presence of AI assistance and is highly vulnerable to AI-induced biases and opinion shifts.
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 04925dce-5018-46b3-8362-3c2b044fe2b0Builds on11
- Language Models are Few-Shot LearnersTom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah et al.NeurIPS 2020 · 64,255 citations
- CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model CapabilitiesMina Lee, Percy Liang, Qian YangCHI 2022 · 340 citations
- A Design Space for Intelligent and Interactive Writing AssistantsMina Lee, Katy Ilonka Gero, John Joon Young Chung, Simon Buckingham Shum et al.CHI 2024 · 133 citations
- From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP ModelsShangbin Feng, Chan Young Park, Yuhan Liu, Yulia TsvetkovACL 2023 · 117 citations
- The Impact of Multiple Parallel Phrase Suggestions on Email Input and Composition Behaviour of Native and Non-Native English WritersDaniel Buschek, Martin Zürn, Malin EibandCHI 2021 · 106 citations
Related papers
- 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
- Co-Writing with Opinionated Language Models Affects Users' ViewsMaurice Jakesch, Advait Bhat, Daniel Buschek, Lior Zalmanson et al.CHI 2023 · 249 citations
- When Stereotypes GTG: The Impact of Predictive Text Suggestions on Gender Bias in Human-AI Co-WritingConnor Baumler, Hal Daumé IIICHI 2026 · 3 citations
- How Does the Disclosure of AI Assistance Affect the Perceptions of Writing?Zhuoyan Li, Chen Liang, Jing Peng, Ming YinEMNLP 2024 · 8 citations
- From Use to Oversight: How Mental Models Influence User Behavior and Output in AI Writing AssistantsShalaleh Rismani, Su Lin Blodgett, Q. Vera Liao, Alexandra Olteanu et al.CHI 2026 · 1 citation
