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arxiv: 2605.15322 · v1 · pith:N3HQVOANnew · submitted 2026-05-14 · 💻 cs.HC

Overreliance in Writing Tasks: Exploring Similarity-Based Measures of AI Influence on Writing and Proposing a Reflective Writing Interface Intervention

Vitor H. A. Welzel , Nicholas Vincent This is my paper

Pith reviewed 2026-05-19 15:52 UTC · model grok-4.3

classification 💻 cs.HC
keywords AI overreliancegenerative AI writingtextual similarityreflective interfacehuman-AI interactionwriting tasksuser study
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The pith

AI assistance is linked to greater reuse of its suggestions in users' final writing, and a reflective interface may increase awareness of that influence.

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

The paper examines how generative AI affects open-ended writing by tracking how much of the AI's suggested text ends up in participants' completed work. A study with 47 people completing analysis and synthesis tasks found that those using AI showed more textual overlap with the suggestions provided. Building on this, the authors created an interactive interface that surfaces AI outputs and prompts reflection during the writing process. A small follow-up think-aloud test with four users indicated the interface can help people notice how they incorporate AI material and engage with it more deliberately. Readers would care because the work supplies measurable ways to observe AI influence and a practical design approach for encouraging thoughtful use.

Core claim

In a mixed-methods study, 47 participants completed writing tasks with or without generative AI assistance. Quantification of textual overlap showed that AI assistance was associated with patterns of suggestion reuse in the final writing. Analysis of participant reflections supported this pattern. A follow-up think-aloud study with a reflective writing interface (n=4) suggested that the interface can increase awareness of how AI outputs are incorporated and support more conscious engagement with the assistance.

What carries the argument

Similarity-based measures of textual overlap between AI suggestions and participants' final writing, serving as a proxy for measuring AI influence on the output.

If this is right

  • AI assistance during writing tasks correlates with higher rates of reusing specific suggested phrases or passages.
  • A reflective interface that highlights AI contributions can raise users' awareness of how those contributions appear in their work.
  • Interface features prompting reflection may lead to more deliberate decisions about incorporating AI-generated material.

Where Pith is reading between the lines

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

  • The overlap measurement approach could be extended into automated tools that flag potential AI influence for users in real time.
  • Similar methods might apply to studying AI effects in other text-based creative tasks such as report drafting or content planning.
  • The reflective interface design points toward broader interface strategies for supporting user agency when working with generative tools.

Load-bearing premise

That the amount of shared text between AI suggestions and a participant's final writing reliably indicates overreliance or influence rather than other reasons such as adopting good ideas or natural stylistic similarity.

What would settle it

A larger controlled study that finds no measurable difference in textual overlap between groups that did and did not receive AI suggestions during the same writing tasks.

Figures

Figures reproduced from arXiv: 2605.15322 by Nicholas Vincent, Vitor H. A. Welzel.

Figure 1
Figure 1. Figure 1: Experiment interface showing: (a) reading of the reference text, (b) presentation of AI suggestions, and (c) participant [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Participants draft text in the main editor (left) while a side panel (right) provides feedback on similarity to an AI [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
read the original abstract

As generative AI (GenAI) systems become increasingly proficient at simulating human-like and well-reasoned text, users may attribute authority to AI outputs, shaping how they engage with writing and reasoning tasks. While prior work has raised concerns about AI overreliance, empirical approaches for observing this phenomenon during open-ended writing remain limited. In this paper, we examine how GenAI assistance influences users' interactions with AI suggestions during writing. We report results from a mixed-methods study in which 47 participants completed analysis and synthesis writing tasks with or without AI assistance. We quantify the textual overlap between AI suggestions and participants' writing and analyze participants' reflections. Our results show that AI assistance is associated with patterns of suggestion reuse. Building on these findings, we design and evaluate an interactive writing interface that may support reflection on the usage of the AI suggestions during writing. Evidence from a small follow-up think-aloud study (n = 4) suggests that the interface can increase users' awareness of how AI outputs are incorporated into their writing and may support more conscious engagement with AI assistance. Together, our findings contribute empirical methods for studying AI adoption in writing contexts and demonstrate how interface design can shape user-AI interaction.

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

3 major / 1 minor

Summary. The paper reports results from a mixed-methods study with 47 participants completing analysis and synthesis writing tasks with or without GenAI assistance. It quantifies textual overlap between AI suggestions and final participant writing to show an association with patterns of suggestion reuse, analyzes participant reflections, and proposes a reflective writing interface evaluated via a small think-aloud study (n=4) suggesting increased awareness of AI incorporation.

Significance. If the textual overlap measure can be shown to capture AI-driven influence beyond baseline task-induced convergence or stylistic alignment, the work offers useful empirical methods for studying AI adoption in open-ended writing and illustrates how interface design might promote more conscious engagement. The mixed-methods approach and the concrete interface proposal are strengths that could inform tool development if measurement validity is strengthened.

major comments (3)
  1. [Methods] Methods: The paper provides no detail on the exact similarity metrics for quantifying textual overlap, statistical controls used, or exclusion criteria applied in the n=47 study; without these, the reported association between AI assistance and suggestion reuse cannot be fully evaluated for robustness.
  2. [Results] Results: The central claim that observed overlap indicates AI influence or overreliance lacks controls such as independent-generation baselines or human-coded distinctions between literal reuse, paraphrase, and conceptual adoption; the overlap could instead reflect task demands or idea convergence in the analysis/synthesis tasks.
  3. [Follow-up Study] Follow-up evaluation: The n=4 think-aloud study is presented as suggestive evidence that the reflective interface increases awareness, but the small sample and lack of quantitative outcome measures limit any generalizable claims about supporting conscious engagement with AI assistance.
minor comments (1)
  1. [Abstract] Clarify in the abstract and methods whether the unassisted condition involved any form of external reference material that could produce comparable overlap by chance.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their detailed and constructive feedback, which has helped us identify areas for clarification and improvement. We address each major comment below, indicating revisions where we can strengthen the manuscript without misrepresenting our work.

read point-by-point responses

  1. Referee: [Methods] Methods: The paper provides no detail on the exact similarity metrics for quantifying textual overlap, statistical controls used, or exclusion criteria applied in the n=47 study; without these, the reported association between AI assistance and suggestion reuse cannot be fully evaluated for robustness.

    Authors: We acknowledge the need for greater methodological transparency. The original manuscript described the overall approach to measuring textual overlap but omitted precise implementation details. In the revision, we will add a dedicated Methods subsection specifying the similarity metrics (cosine similarity over sentence embeddings combined with n-gram overlap), the statistical models (including regression controls for task type and participant variables), and exclusion criteria (e.g., incomplete tasks or technical failures). This will enable full evaluation of robustness. revision: yes

  2. Referee: [Results] Results: The central claim that observed overlap indicates AI influence or overreliance lacks controls such as independent-generation baselines or human-coded distinctions between literal reuse, paraphrase, and conceptual adoption; the overlap could instead reflect task demands or idea convergence in the analysis/synthesis tasks.

    Authors: We agree this is a substantive limitation in causal interpretation. Our design included a no-AI control condition showing significantly lower overlap, which we will highlight more explicitly as evidence against purely task-driven convergence. However, we did not collect independent-generation baselines or perform human coding of reuse types. We will revise the Results and Discussion to explicitly discuss these gaps as limitations, temper claims about 'overreliance,' and frame the overlap measure as one indicator supported by qualitative reflections rather than conclusive proof. We maintain the between-condition difference provides useful evidence of AI-specific patterns but will avoid overstatement. revision: partial

  3. Referee: [Follow-up Study] Follow-up evaluation: The n=4 think-aloud study is presented as suggestive evidence that the reflective interface increases awareness, but the small sample and lack of quantitative outcome measures limit any generalizable claims about supporting conscious engagement with AI assistance.

    Authors: We fully agree that the small sample and qualitative focus limit generalizability. The follow-up was explicitly positioned as an exploratory think-aloud evaluation to gather design insights, not a confirmatory test. In the revision, we will strengthen language to emphasize its preliminary, suggestive nature, explicitly note the absence of quantitative measures, and outline directions for future larger-scale studies with controlled quantitative outcomes. No overgeneralized claims will remain. revision: yes

Circularity Check

0 steps flagged

No significant circularity in empirical mixed-methods study

full rationale

This is an empirical mixed-methods study reporting results from 47 participants in assisted vs. unassisted writing tasks plus a small follow-up think-aloud study. The central observations concern measured textual overlap between AI suggestions and final writing, plus participant reflections on an interface intervention. No mathematical derivations, equations, fitted parameters, or self-citation chains appear in the provided text that would reduce any claim to its inputs by construction. The overlap measure is presented as an empirical proxy for suggestion reuse rather than a self-definitional or tautological restatement. The study is self-contained against external benchmarks (participant data and reflections) and does not invoke uniqueness theorems or ansatzes from prior self-work. This is the expected honest non-finding for an observational HCI paper without derivation chains.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claims rest on the assumption that textual similarity is a meaningful indicator of AI influence and that increased awareness from the interface equates to reduced overreliance; no free parameters or invented entities are introduced.

axioms (1)
  • domain assumption Textual overlap metrics reliably capture the degree of AI suggestion reuse and influence during writing.
    Invoked to link measured similarity to patterns of overreliance in the main study results.

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