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REVIEW 3 major objections 6 minor 99 references

LLMs in Wikipedia: Investigating How LLMs Impact Participation in Knowledge Communities

T0 review · 3 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read LLM use on Wikipedia helps expert editors thrive while pushing newcomers into a participation paradox that gets their edits rejected.

desk verdict A timely, honest qualitative study of LLM use by Wikipedia editors; the 'participation paradox' is a useful framing, but the causal claim about community responses rests on self-reports and is partly conceded in Section 5.2. read the letter →

arxiv 2509.07819 v1 pith:RLXWNNPS submitted 2025-09-09 cs.HC

Moyan Zhou , Soobin Cho , Loren Terveen This is my paper · ORCID
classification cs.HC
keywords largelanguagemodelsWikipediaknowledgecommunitiesparticipationdividenewcomeronboardinglegitimateperipheralhuman-AIcollaborationqualitativeinterviews
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper asks what happens to participation in a knowledge community when members use large language models to write and edit. Drawing on interviews with 16 Wikipedia editors who used LLMs, it argues that the outcome splits along expertise: experienced editors use LLMs to explore new topics, check their work, and earn praise, while newcomers find that the same tools lower the barrier to entry but raise the bar for what counts as acceptable participation. The key claim is a 'participation paradox': LLMs let newcomers attempt central tasks such as drafting articles before they have the editorial judgment those tasks require, and the community responds by rejecting their edits. If correct, the paper implies that simply giving people access to AI writing tools does not democratize knowledge production; it can reproduce or even widen existing inequalities.

What carries the argument

The load-bearing mechanism is the 'participation paradox': LLMs simultaneously lower barriers to entry and raise the demands of participation by requiring editorial judgment that newcomers lack. The paper frames this through legitimate peripheral participation, the classic model in which newcomers start with peripheral tasks and gradually move toward central responsibilities while learning community norms through social interaction. The paper also identifies three concrete judgment strategies editors must apply to LLM output - evaluation, verification, and modification - and argues that these demand tacit knowledge about Wikipedia's policies and culture that newcomers have not yet acquired. This combination - interrupted learning pathways, a shift from peripheral tasks to editorial judgment, and community sensitivity to AI origin - is what produces the expertise-based divide.

What would settle it

A quantitative audit of Wikipedia's edit history: flag contributions whose edit summaries or text indicate LLM use, split by editor tenure, and compare reversion rates. If newcomers' LLM-assisted edits are reverted at the same rate as experienced editors' once content quality is controlled, the claimed expertise-based participation divide would not be supported.

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Extended reading notes

Core claim

The paper's central claim is that LLM use in Wikipedia creates a participation divide mediated by expertise. Experienced editors enhance their participation: they venture into new topics, treat LLMs as sources of fresh perspectives, gain confidence in unfamiliar areas, and receive positive responses from other editors. Newcomers, by contrast, face a paradox: LLMs lower the barriers to entry, but they also demand that newcomers act as editorial judges who evaluate, verify, and modify AI output before publishing it, a role that presupposes the very wiki knowledge newcomers have not yet developed. As a result, LLM-assisted contributions from newcomers are more likely to be flagged, criticized, and reverted. The authors argue that this dynamic breaks the traditional trajectory of legitimate peripheral participation, in which newcomers earn legitimacy by starting with small, low-risk tasks and gradually absorbing community norms; LLMs skip that pathway and push newcomers straight into central, high-stakes judgment.

Load-bearing premise

The strongest assumption is that the 16 volunteer interviewees' recollections accurately represent how Wikipedia editors generally use LLMs and how the community reacts; if the sample skews toward enthusiastic adopters or particular editor types, the reported divide may be an artifact of who agreed to talk.

Editorial extensions

If this is right

  • LLM-assisted edits from newcomers will continue to be flagged and reverted quickly unless tools or norms change, because the community treats AI origin as a signal of low quality.
  • Experienced editors will keep gaining more from LLM assistance, including broader topics, faster drafting, and higher confidence, which widens the gap between them and newcomers.
  • LLM tools that simply generate finished articles are the wrong design; assistants should scaffold tasks, teach community norms, and adapt to the editor's expertise level.
  • Wikipedia's norm-formation problem is urgent: the absence of shared rules leaves individual editors to improvise, and the resulting ambiguity feeds mistrust of all LLM-assisted work.
  • If LLMs are integrated without attention to learning, they can undermine the social processes that have sustained knowledge communities, not just the quality of individual edits.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the divide is real, edit-reversion data should show a measurable tenure-by-LLM interaction: newcomers' LLM-flagged edits are reverted more often and faster than experienced editors' LLM-flagged edits, which is testable in Wikipedia's public revision history.
  • The design proposals imply a concrete test: an LLM assistant that decomposes article creation into source-finding, summarizing, and structuring steps should improve newcomer retention and edit acceptance relative to a direct-draft assistant in a randomized field experiment.
  • The paper's logic also predicts that community norms will harden around process rather than outcome: editors will be sanctioned for using LLMs without demonstrated review, regardless of content quality, which could suppress good-faith contributions from non-native speakers who rely on LLMs for language support.
  • Because the sample is self-selected LLM adopters, the same mechanisms might look different for editors who tried LLMs and quit; surveying that group could separate tool effects from adopter effects.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. This paper reports a qualitative interview study of 16 Wikipedia editors who have used large language models in their editing work. The authors ask how LLMs affect content contribution (RQ1), what strategies editors use to align LLM output with community norms (RQ2), and how other editors respond to LLM-assisted contributions (RQ3). The central finding is an expertise-based participation divide: experienced editors use LLMs to explore new topics, gain confidence, and improve editing quality, while newcomers experience a 'participation paradox' in which LLMs lower entry barriers but simultaneously demand editorial judgment that newcomers have not yet developed, leading to community rejection. The authors interpret this through Legitimate Peripheral Participation (LPP) and situated learning, arguing that LLM use interrupts newcomers' gradual learning pathways and shifts them from peripheral tasks to high-stakes editorial judgment. They close with design implications for scaffolding, teaching community norms, and expertise-aware LLM interaction, and they acknowledge in Section 5.3 that prevalence was not quantified and that only LLM-using editors were interviewed.

Significance. If the findings hold, the paper makes a useful contribution to CSCW/HCI scholarship on human-AI collaboration and peer production. It articulates a specific mechanism, the participation paradox, that goes beyond 'LLMs help some and hurt others' by linking the divide to a mismatch between the tacit knowledge required for editorial judgment and the capabilities of newcomers. The extension of LPP to a setting where the tool itself changes the trajectory of participation is a plausible and interesting theoretical move. Methodologically, the paper has strengths: the interviews are quoted extensively, the thematic analysis procedure is standard, the coding process is described transparently, and the authors honestly concede the main limitations of self-report and sample composition. The design implications are concrete and grounded in the data. However, the central empirical claim about differential community responses relies on retrospective self-reports from a self-selected sample, and one passage in the Discussion appears to undercut the causal reading of the LPP argument.

major comments (3)
  1. [Section 4.3 / RQ3] The community-response asymmetry, namely that other editors reject LLM-assisted edits from newcomers but approve those from experienced editors, is supported only by retrospective self-reports from the 16 LLM-using interviewees. No talk-page logs, reversion statistics, or interviews with the responding editors are provided. This matters because the paper itself cites Halfaker et al. [34] showing that newcomer edits, especially new-article creations, are reverted at high rates even without LLMs. The rejections reported by P11, P01, P10, and P15 could therefore reflect ordinary newcomer gatekeeping rather than an LLM-specific 'participation paradox.' I recommend either (a) obtaining behavioral data, for example comparing reversion/rejection rates for LLM-assisted versus non-LLM-assisted newcomer edits, or (b) explicitly reframing the finding as participants' perceptions of community response rather than as the community's actual response. The acknowledgment in Section 5.3 that non-users were excluded does not address this missing behavioral baseline.
  2. [Section 5.2 (final paragraph), with Section 5.1.1] The final paragraph of Section 5.2 states that 'newcomers wanted to create articles and LLMs may just be a tool they utilize to achieve their goals. If not LLMs, they would still start contributing complex tasks from day one.' This concession is in direct tension with the causal claim in Section 5.1.1 that LLMs interrupt the LPP trajectory by enabling newcomers to skip peripheral tasks. If newcomers would attempt complex tasks regardless of LLM availability, then the interruption of gradual learning is not caused by LLM use, and the claim that LLMs 'shortcut' situated learning is overstated. The paper needs to separate two possibilities: LLMs enabling a pre-existing preference for complex tasks, versus LLMs creating that preference. As written, the causal framing in the Discussion is stronger than the evidence and the paper's own closing caveat allow.
  3. [Section 3.1 / Table 1] The expertise-based divide that organizes the results is never operationalized. The paper frequently contrasts 'newcomers' with 'experienced editors,' but Table 1 shows that P09 has a 0-2 year tenure while making 5.3K+ edits daily, P10 has 2-5 years of tenure with monthly edits, and P14 and P15 have 0-2 years with 200+ edits. These participants are quoted as newcomers in the results, yet by edit count some of them are highly active. The paper should define the newcomer/experienced boundary (tenure, edit count, or a combination) and show that the qualitative patterns are stable under that definition. Without this, the central 'participation divide' claim is vulnerable to circular categorization.
minor comments (6)
  1. [Section 4.3.2] There is a duplicated word in the sentence beginning 'However, However, even experienced editors occasionally faced false accusations...'.
  2. [Section 4.1.3] The word 'satisfication' appears in the sentence 'This cognitive support, in turn, increased confidence and satisfication for experienced editors' and should be 'satisfaction'.
  3. [Table 1 caption] The caption reads 'Participant summary, adopted from [75].' If the table is adopted from a prior paper, the source should be clarified; if it is original to this study, the wording should be corrected.
  4. [Section 3.1] The paper alternates between 'WikiMedia project page' and 'Wikimedia'; please standardize the spelling.
  5. [References] There are duplicate reference entries for the Wikipedia policy pages: NPOV appears as [85] and [86], Verifiability as [89] and [90], and Notability as [88]. These should be consolidated.
  6. [Section 2.2.1] The term 'WikiCotent' appears in the sentence describing super-labels and should be 'WikiContent' (or similar).

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: qualitative findings are independently derived from interviews; LPP is used only as an interpretive lens.

full rationale

This is a qualitative interview study with no equations, fitted parameters, or formal derivation chain. The central findings—an expertise-based participation divide and a newcomer 'participation paradox'—are generated through inductive thematic analysis of 16 interviews, with participant quotes and reported experiences serving as the evidence base. The Legitimate Peripheral Participation (LPP) and situated learning frameworks are introduced in Section 5.1 as interpretive lenses for making sense of those findings; they are not defined in terms of the paper's conclusions, and the conclusions do not reduce to the theory. Self-citations in the paper (e.g., SuggestBot [18], prior Wikipedia value research [64], the participant-summary table format credited to [75], and background citations such as [34]) are contextual or methodological rather than load-bearing; no central claim depends on an unverified assertion imported from the authors' prior work. The limitations acknowledged in Section 5.3 concern sample composition and generalizability, not circular derivation. The skeptical concern about the absence of a non-LLM baseline for community responses is an evidentiary or validity issue, not a circularity issue under the specified rubric.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper is qualitative and introduces no numeric free parameters or new physical or ontological entities. The conceptual label 'participation paradox' is an interpretive framing, not an invented entity. The central claims rest on self-reported interview data and on the validity of LPP as an interpretive lens.

assumptions (3)
  • domain assumption Participant self-reports accurately reflect their editing practices and the responses of other Wikipedia editors.
    All three research questions are answered through interview data; there is no independent verification from edit history, talk pages, or other editors.
  • domain assumption Inductive thematic analysis with researcher consensus produces valid themes.
    The authors describe open coding in ATLAS.ti and collaborative grouping in Miro, but the validity of the resulting themes relies on standard qualitative research conventions rather than formal measures.
  • domain assumption Legitimate Peripheral Participation and situated learning are appropriate theories for interpreting Wikipedia participation.
    The paper uses Lave and Wenger's theories as the lens for the discussion, and its contribution depends on that lens being accepted.

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Cite this review

Pith. "Pith review of LLMs in Wikipedia: Investigating How LLMs Impact Participation in Knowledge Communities." pith.science (2026). https://pith.science/paper/RLXWNNPS

@misc{pith2026250907819,
  author       = {Pith},
  title        = {Pith review of: LLMs in Wikipedia: Investigating How LLMs Impact Participation in Knowledge Communities},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RLXWNNPS}},
  note         = {Machine review of arXiv:2509.07819}
}
read the original abstract

Large language models (LLMs) are reshaping knowledge production as community members increasingly incorporate them into their contribution workflows. However, participating in knowledge communities involves more than just contributing content - it is also a deeply social process. While communities must carefully consider appropriate and responsible LLM integration, the absence of concrete norms has left individual editors to experiment and navigate LLM use on their own. Understanding how LLMs influence community participation is therefore critical in shaping future norms and supporting effective adoption. To address this gap, we investigated Wikipedia, one of the largest knowledge production communities, to understand 1) how LLMs influence the ways editors contribute content, 2) what strategies editors leverage to align LLM outputs with community norms, and 3) how other editors in the community respond to LLM-assisted contributions. Through interviews with 16 Wikipedia editors who had used LLMs for their edits, we found that 1) LLMs affected the content contributions for experienced and new editors differently; 2) aligning LLM outputs with community norms required tacit knowledge that often challenged newcomers; and 3) as a result, other editors responded to LLM-assisted edits differently depending on the editors' expertise level. Based on these findings, we challenge existing models of newcomer involvement and propose design implications for LLMs that support community engagement through scaffolding, teaching, and context awareness.

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.