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Co-Writing with AI, on Human Terms: Aligning Research with User Demands Across the Writing Process

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arxiv 2504.12488 v2 pith:67PIJ7Z3 submitted 2025-04-16 cs.HC cs.AI

classification cs.HCcs.AI
keywords writerswritingacrossagencyco-writingownershipprocesstools
verification ladder T0 review T1 audit T2 compute T3 formal
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As generative AI tools like ChatGPT become integral to everyday writing, critical questions arise about how to preserve writers' sense of agency and ownership when using these tools. Yet, a systematic understanding of how AI assistance affects different aspects of the writing process - and how this shapes writers' agency - remains underexplored. To address this gap, we conducted a systematic review of 109 HCI papers using the PRISMA approach. From this literature, we identify four overarching design strategies for AI writing support: structured guidance, guided exploration, active co-writing, and critical feedback - mapped across the four key cognitive processes in writing: planning, translating, reviewing, and monitoring. We complement this analysis with interviews of 15 writers across diverse domains. Our findings reveal that writers' desired levels of AI intervention vary across the writing process: content-focused writers (e.g., academics) prioritize ownership during planning, while form-focused writers (e.g., creatives) value control over translating and reviewing. Writers' preferences are also shaped by contextual goals, values, and notions of originality and authorship. By examining when ownership matters, what writers want to own, and how AI interactions shape agency, we surface both alignment and gaps between research and user needs. Our findings offer actionable design guidance for developing human-centered writing tools for co-writing with AI, on human terms.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 4 citations worldwide. Full citation record

  1. Componentization: Decomposing Monolithic LLM Responses into Manipulable Semantic Units

    cs.HC 2025-09 conditional novelty 6.0 of 10

    Generative model outputs can be decomposed into typed, linkable components that users edit, toggle, and regenerate before recomposition, as implemented in the MAODchat prototype.

  2. HACo-Det: A Study Towards Fine-Grained Machine-Generated Text Detection under Human-AI Coauthoring

    cs.CL 2025-06 conditional novelty 6.0 of 10

    Current machine-generated text detectors, especially metric-based ones, perform poorly on word-level detection in coauthored texts, while finetuned DeBERTa achieves strong but imperfect performance.

  3. "Pragmatic Tools or Empowering Friends?" Discovering and Co-Designing Personality-Aligned AI Writing Companions

    cs.HC 2025-09 conditional novelty 5.0 of 10

    Writers grouped into four MBTI-based profiles showed divergent preferences for AI writing companion features, demonstrated by two contrasting prototypes in a small proof-of-concept study.

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