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arxiv: 2604.11009 · v1 · submitted 2026-04-13 · 💻 cs.HC

Recognition: no theorem link

From Planning to Revision: How AI Writing Support at Different Stages Alters Ownership

Katy Ilonka Gero , Tao Long , Carly Schnitzler , Paramveer Dhillon
Authors on Pith no claims yet

Pith reviewed 2026-05-10 16:12 UTC · model grok-4.3

classification 💻 cs.HC
keywords AI writing assistanceownershipwriting stagesplanningdraftingrevisinghuman-AI collaboration
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The pith

AI support for drafting reduces writers' sense of ownership more than support for planning or revising.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests whether the stage of AI help in writing changes how much ownership people feel over the final essay. In a between-subjects experiment, 253 participants wrote short essays with AI assistance either at planning, drafting, or revising; any form of help lowered ownership, but drafting help lowered it the most while planning help lowered it the least. This pattern tracked the volume of text and ideas supplied by the AI, with greater AI input producing both lower ownership and higher essay quality. The work shows that an AI draft generated from a user's own outline still introduced more ideas than direct planning support. The authors conclude that stage matters when introducing AI tools for writing.

Core claim

While any AI assistance decreased ownership, planning support only minimally decreased ownership, while drafting support saw the largest decrease. This variation maps onto the amount of text and ideas contributed by AI, where more text and ideas from AI decreased ownership. Notably, an AI-generated draft based on participants' own outline resulted in significantly more AI-contributed ideas than AI support for planning. At the same time, more AI contributions improved essay quality.

What carries the argument

Writing-stage-specific AI assistance, measured by self-reported ownership and quantified counts of AI-contributed text and ideas.

If this is right

  • Greater AI text and idea contribution improves measurable essay quality.
  • An AI draft built on a user's outline still supplies more ideas than AI planning support alone.
  • Stage of assistance should be considered when designing AI writing tools to manage ownership effects.
  • Educators and writers may need stage-aware guidelines for when AI help is introduced.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Designers could limit AI idea generation in early stages to reduce ownership loss while retaining quality gains.
  • Longer-term studies could test whether reduced ownership affects continued writing practice or attribution norms.
  • The findings suggest testing stage-specific defaults in consumer AI writing products.

Load-bearing premise

That the three writing stages can be cleanly separated in the experimental conditions without carryover effects and that self-reported ownership scores capture the relevant psychological experience.

What would settle it

A replication that holds the amount of AI-contributed text and ideas constant across stages and finds no difference in ownership scores.

Figures

Figures reproduced from arXiv: 2604.11009 by Carly Schnitzler, Katy Ilonka Gero, Paramveer Dhillon, Tao Long.

Figure 1
Figure 1. Figure 1: Study flow: all participants had to first outline, then draft, then spend time revising their essay. Participants were split [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: A screenshot from the planning condition. Participants wrote their outline in a textbox on the left. A panel on the [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Screenshot of the revision tool panel, showing some [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Results for RQ1. Any AI support decreases ownership compared to a No-AI baseline, with the AI-generated draft [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Results for RQ2. Attribution of text and ideas to AI tracks with our ownership results, with highest ownership aligning [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: (Note that our linear model that controls for prompt vari [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
Figure 6
Figure 6. Figure 6: Results for RQ3; note that our model controls for prompt variant as well as final word count and total minutes on [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Outline Stage: The AI suggests supporting ideas in bullet points, shown in the right panel. In conditions where AI [PITH_FULL_IMAGE:figures/full_fig_p017_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Draft Stage: The user manually drafts their essay based on the outline shown on the right from the previous step. In [PITH_FULL_IMAGE:figures/full_fig_p017_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Revision Stage: User revises essay in main textbox. In the no-AI condition, their outline is provided in the right panel, [PITH_FULL_IMAGE:figures/full_fig_p018_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Revision Stage (AI): Examples of each of the three revision tools. [PITH_FULL_IMAGE:figures/full_fig_p018_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Survey and essay results across conditions. (a) Likert scale distributions, (b) self-reported attribution of ideas vs text, [PITH_FULL_IMAGE:figures/full_fig_p032_11.png] view at source ↗
read the original abstract

Although AI assistance can improve writing quality, it can also decrease feelings of ownership. Ownership in writing has important implications for attribution, rights, norms, and cognitive engagement, and designers of AI support systems may want to consider how system features may impact ownership. We investigate how the stage at which AI support for writing is provided (planning, drafting, or revising) changes ownership. In a study of short essay writing (between subjects, n = 253) we find that while any AI assistance decreased ownership, planning support only minimally decreased ownership, while drafting support saw the largest decrease. This variation maps onto the amount of text and ideas contributed by AI, where more text and ideas from AI decreased ownership. Notably, an AI-generated draft based on participants' own outline resulted in significantly more AI-contributed ideas than AI support for planning. At the same time, more AI contributions improved essay quality. We propose that writers, educators, and designers consider writing stage when introducing AI assistance.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper reports results from a between-subjects user study (n=253) on short-essay writing that examines how providing AI assistance at different stages (planning, drafting, or revising) affects writers' sense of ownership. The central empirical claim is that any AI assistance reduces ownership relative to a no-AI baseline, with planning-stage support producing only minimal reduction while drafting-stage support produces the largest reduction; this pattern is attributed to measurable differences in the volume of text and ideas contributed by the AI. The study additionally reports that greater AI contributions improve essay quality and that an AI-generated draft based on a participant's own outline yields more AI-contributed ideas than planning support alone.

Significance. If the reported stage-specific effects and their mapping to contribution volume hold after fuller methodological disclosure, the work offers a useful empirical contribution to HCI research on AI-augmented creative tasks. It supplies concrete guidance for tool designers and educators on choosing intervention timing to balance quality gains against ownership loss, and it strengthens the evidence base linking objective contribution metrics to subjective psychological outcomes in writing.

major comments (2)
  1. [Methods] Methods section: the abstract and study description omit the precise ownership measurement instrument (number and wording of items, scale anchors, reliability statistics), the exact statistical tests and effect-size reporting for the stage comparisons, and the participant exclusion criteria that produced the final n=253. These omissions are load-bearing because the central claim rests on detecting differential ownership reductions that are then mapped to AI contribution volume.
  2. [Results] Results and Discussion: the claim that an AI-generated draft from participants' own outlines produced significantly more AI-contributed ideas than planning support requires explicit reporting of the relevant means, standard deviations, and inferential statistics (including any correction for multiple comparisons) to allow readers to judge whether the difference is robust enough to support the proposed mechanism.
minor comments (2)
  1. [Abstract] Abstract: the phrasing 'planning support only minimally decreased ownership' would be clearer if accompanied by the actual mean difference or effect size relative to the no-AI baseline.
  2. [Methods] The manuscript would benefit from a brief table or figure summarizing the four experimental conditions, the exact AI prompts or interfaces used at each stage, and the measured outcome variables.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the careful review and for identifying areas where additional methodological and statistical detail will strengthen the manuscript. We agree that fuller disclosure is warranted for the central claims and will incorporate the requested information in the revised version.

read point-by-point responses

  1. Referee: [Methods] Methods section: the abstract and study description omit the precise ownership measurement instrument (number and wording of items, scale anchors, reliability statistics), the exact statistical tests and effect-size reporting for the stage comparisons, and the participant exclusion criteria that produced the final n=253. These omissions are load-bearing because the central claim rests on detecting differential ownership reductions that are then mapped to AI contribution volume.

    Authors: We agree that these details are necessary for reproducibility and to allow readers to evaluate the ownership findings. In the revised manuscript we will expand the Methods section to report: (1) the full ownership instrument, including the exact number of items, wording, scale anchors (e.g., 1–7 Likert), and reliability (Cronbach’s α); (2) the precise statistical tests (ANOVA or planned contrasts) and effect sizes (η² or Cohen’s d) for all stage comparisons; and (3) the initial sample size, exclusion criteria, and how the final n=253 was obtained. These additions will directly support the mapping from contribution volume to ownership. revision: yes

  2. Referee: [Results] Results and Discussion: the claim that an AI-generated draft from participants' own outlines produced significantly more AI-contributed ideas than planning support requires explicit reporting of the relevant means, standard deviations, and inferential statistics (including any correction for multiple comparisons) to allow readers to judge whether the difference is robust enough to support the proposed mechanism.

    Authors: We acknowledge that the current text states the significant difference without the supporting statistics. In the revised Results section we will add the relevant descriptive statistics (means and SDs for AI-contributed ideas in the outline-based draft vs. planning conditions), the inferential test (t-test or ANOVA), exact p-value, effect size, and any multiple-comparison correction applied. This will allow readers to assess the robustness of the evidence for the proposed mechanism. revision: yes

Circularity Check

0 steps flagged

Empirical user study with no derivations or self-referential elements

full rationale

The paper reports a between-subjects experiment (n=253) measuring ownership, AI text/idea contributions, and essay quality across planning, drafting, and revising conditions. All central claims are direct statistical comparisons of collected participant data; there are no equations, fitted parameters, predictions derived from inputs, uniqueness theorems, or self-citations that bear the load of any result. The mapping from AI contribution volume to ownership is an observed correlation in the data, not a constructed equivalence or renamed prior result.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

This empirical HCI study introduces no free parameters, invented entities, or ad-hoc axioms beyond standard assumptions of experimental design and statistical comparison in between-subjects studies.

axioms (1)
  • domain assumption Between-subjects design isolates effects of AI support stage without carryover
    Invoked to attribute ownership differences to the assigned stage

pith-pipeline@v0.9.0 · 5477 in / 1225 out tokens · 52592 ms · 2026-05-10T16:12:20.735772+00:00 · methodology

discussion (0)

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Reference graph

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Showing first 80 references.