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REVIEW 3 major objections 7 minor 132 references

Thoughtful, Confused, or Untrustworthy: How Text Presentation Influences Perceptions of AI Writing Tools

T0 review · 3 major / 7 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read AI text speed shifts users' trust and quality judgments.

desk verdict A well-designed study of how text streaming speed shapes perceptions of AI writing tools, but the analysis ignores the within-subjects structure and the small 'perceived quality' effect needs a reanalysis before that claim is taken seriously. read the letter →

arxiv 2504.20365 v1 pith:R37QK2EY submitted 2025-04-29 cs.HC

David Zhou , John R. Gallagher , Sarah Sterman This is my paper · ORCID
classification cs.HC
keywords AIwritingtoolstextpresentationspeedstreamingperceivedqualitytrustworthinessanthropomorphismcreativeuserstudy
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 whether the way AI-generated text appears on screen—typed out slowly, quickly, in reverse, or in random order—changes how readers judge the tool and its output. Across 297 online participants imagining themselves co-writing in creative and professional scenarios, the authors find that medium-speed presentation (600 words per minute, close to reading speed) is the most comfortable and earns the highest perceived text quality. Slower and medium speeds make the AI seem more human-like, while backwards and random presentations are rated less trustworthy. The authors conclude that text appearance speed is not a neutral interface detail: it influences perceptions of quality and trust, which can shape whether users accept or reject AI-generated text.

What carries the argument

The central object is the text presentation style, operationalized as five within-subject conditions: slow (160 wpm), medium (600 wpm, near average reading speed), fast (6,000 wpm, approximating large language model token generation), backwards (characters appear in reverse), and random (characters inserted in random order), with the content held fixed. These conditions isolate the perceptual contribution of appearance speed and order from the meaning of the text. The mechanism is that users read along with the appearing text, so the pace and order either match or disrupt their reading process, producing comfort or discomfort and cueing human-like versus machine-like attributions.

What would settle it

A replication in which participants actually write their own text and receive unique AI completions, with the same speed conditions, would falsify the generalization if the medium-speed advantage on quality and trust disappears. A simpler check: have readers rate the final complete texts without seeing the animation; if speed-blind ratings still differ by condition, the effect would be in the content, not the presentation.

Watch

Extended reading notes

Core claim

The central discovery is that the speed and order of text appearance affect users' perceptions of an AI writing tool independently of the text content. In the experiment, the identical text presented at medium speed was rated highest on comfort and quality; slow and medium speeds were perceived as more human-like; and the two deliberately non-anthropomorphic styles—backwards and random character order—were rated as less trustworthy. The authors report that users read along with the generation, attribute human-like qualities such as thoughtfulness to slower appearance, and show divided preferences tied to their writing values rather than a consistent genre effect. Their conclusion is that interface presentation decisions influence judgments of system quality and trustworthiness, and thereby influence how generated text is used.

Load-bearing premise

The study's load-bearing premise is that imagining oneself as a co-writer produces the same perceptions as actually writing; the authors note that imagining does not create the same experience as writing, so the measured effects may not extend to real writing sessions.

Editorial extensions

If this is right

  • Medium-speed text appearance, near average reading speed, produces the highest reading comfort and perceived text quality; designers who want favorable quality judgments should not default to maximum speed.
  • Backwards and random text presentation reduce perceived trustworthiness and quality, so non-anthropomorphic streaming styles carry a perception cost even when the final text is identical.
  • Slow and medium speeds make the AI seem more human-like; users may accept more output from tools that appear thoughtful, a consequence the authors flag as potentially unintentional manipulation.
  • Text presentation effects on comfort, quality, humanness, and trust were not substantially moderated by genre, indicating the effects are not confined to one writing context.
  • Because perceptions influence use, tools that display text too fast or too slowly may change how much users scrutinize suggestions, influencing acceptance and rejection decisions.

Reading between the lines

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

  • The authors' setup fixes the text content; an untested extension is whether the same perceptual effects appear when users write their own text and receive personalized completions, where involvement and ownership may override presentation cues.
  • If the trust effect holds in real use, one testable design response is to decouple display speed from model computation speed and let users set the pace, which would break the current coupling between latency optimization and perception.
  • The findings suggest a concrete hypothesis for neighboring domains: adding human-like pauses or backspacing to AI output, as some tools already do, may increase perceived thoughtfulness and thereby raise acceptance rates of fallible content.
  • A direct follow-up prediction: blind evaluators who rate the same final texts without seeing the animation should show no speed effect, confirming that the effect lives in presentation rather than content.
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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 / 7 minor

Summary. This paper examines how the presentation style of AI-generated text (speed and character order) affects users' perceptions of an AI writing tool. In an online study with 297 participants, each participant saw five text appearance conditions (slow, medium, fast, backwards, random) in two genres (creative, professional) and rated reading comfort, perceived text quality, humanness, trust, respect, liking, and intention to use. The authors report that medium speed is most comfortable and yields the highest perceived quality, slow and medium are perceived as most human-like, forward presentation is more trustworthy than backwards or random, and that genre shows no consistent interaction. The paper includes qualitative thematic analysis and an exploratory appendix.

Significance. If the reported effects are valid, this work makes a meaningful contribution to HCI and AI interface design by demonstrating that a seemingly cosmetic design choice—the speed and order of streamed AI text—can change users' judgments of output quality and trustworthiness. The study's strengths include pre-registered-style hypothesis finalization one month before data collection, fixed texts, randomized speed-text pairs, a within-subjects design, Bonferroni correction, and an ART robustness check. The large comfort effect (η²p = 0.19) is substantial and the qualitative findings are rich. However, a serious statistical flaw—the analysis treats repeated-measures data as independent observations—undermines the reported p-values and post-hoc groupings, particularly for the small effects such as perceived quality. The central qualitative narrative is likely defensible, but the quantitative evidence needs to be re-analyzed before the paper can be accepted.

major comments (3)
  1. [Section 4.3.1 / Section 5.1–5.4] The statistical analysis treats each of the 2970 trials (297 participants × 10 within-subject trials) as independent observations. The reported error df of 4,2965 for presentation style corresponds to N = 2970 independent observations, not to the repeated-measures structure described in Section 4.1, where every participant saw all five styles in both genres. This inflates the test statistics and invalidates the exact p-values, effect sizes, and Tukey post-hoc letter groupings. This is load-bearing for the paper's smaller effects, especially H2 (perceived quality: η²p = 0.02, medium-minus-fast mean gap 0.14 on a -2 to 2 scale) and for H4–H8. Please re-analyze with a mixed-effects model (participant as a random effect, with scenario/genre as appropriate) or a repeated-measures ANOVA, and report corrected F, p, effect sizes, and post-hoc comparisons. The Aligned Rank Transform robustness check must also account for the within-subjects design. The large comfort and humanness effects may survive, but the exact numbers and the abstract's claim about perceived quality are not trustworthy as reported.
  2. [Section 5.2 (H2) and Section 7] The statement that medium presentation 'resulted in the highest perceived quality' is a post-hoc finding from pairwise comparisons after a null-hypothesis ANOVA (H2 predicted no effect). Once the repeated-measures analysis is correctly performed, the post-hoc comparisons must be recomputed with the proper error terms. The abstract and conclusion should reflect the corrected results, and any claim that speed is 'correlated with' quality should be expressed as a perceptual effect conditional on the corrected analysis.
  3. [Sections 3.3 and 5.4] The hypotheses H4, H6, and H8 explicitly involve genre-by-presentation interactions (e.g., fast for professional, slow/medium for creative), yet the results sections do not report the interaction term's F and p values. The conclusion states 'we do not find evidence of a consistent interaction between text appearance speed and genre system,' but the basis for this claim is not presented. Please report the interaction F, df, and p for each dependent variable, or explicitly state that interactions were not tested and adjust the interpretation accordingly.
minor comments (7)
  1. [Abstract] The abstract says 'speed is correlated with perceived humanness and trustworthiness of the AI tool, as well as the perceived quality of the generated text.' This is imprecise because the manipulated factor is presentation style, which includes character order (backwards, random) as well as speed; 'correlated' is also weaker than the experimental design allows—suggest 'affected' or 'influenced'.
  2. [Section 3.1.1 (Medium)] Medium is described as 'slightly faster than average reading speed,' but the cited reference in Section 2.2 gives average reading speeds of 200–400 wpm; 600 wpm is 1.5 to 3 times that range. Please revise the justification for the medium anchor.
  3. [Section 3.1.1 (Random)] The description 'random insertion via insertion-sort' is unclear, because insertion sort is a deterministic ordering algorithm, not a random insertion process. Please clarify the exact character placement procedure.
  4. [Section 4.3.1] The phrase 'independent multi-way ANOVAs' is ambiguous and could be read as 'independent-observations ANOVAs,' which is precisely the problem described above. Consider renaming to 'separate ANOVAs' and explicitly noting the need for a repeated-measures approach.
  5. [Table 2] For the genre rows in H2, H4, H5, and H8, significant p-values are shown as '***' but no compact letter display is provided; the text says post-hoc comparisons were conducted for significant ANOVAs. Please add the corresponding letter groupings or explain why they are omitted.
  6. [Figure 2] The figure contains placeholder text such as '/gid00035' that appears to be a rendering artifact; please replace these strings with proper textual labels.
  7. [Section 6.3] The limitation about 'imagining writing does not create the same experience as writing' is appropriately acknowledged. Consider explicitly tying this to the quantitative results—e.g., effects might differ when users are actively composing rather than reading prefilled text.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: an empirical perception study with hypotheses grounded in prior literature, no fitted parameters, no self-citation load-bearing derivation, and no prediction that reduces to its inputs.

full rationale

This paper is an empirical user study, not a derivation or modeling paper. The central claims—that text appearance speed affects perceived comfort, quality, humanness, trustworthiness, and adoption attitudes—are tested through a preregistered-style experiment with five presentation-speed conditions and two genres. The independent variables are constructed from display latencies and character orders, and the dependent variables are Likert-scale survey responses. There is no equation that maps inputs to outputs, no fitted parameter that is later renamed as a prediction, and no uniqueness theorem or ansatz imported from the authors' prior work to force a conclusion. The hypotheses in Sec. 3.3 are grounded in external literature (speech-rate perception, reading speed, typing-speed research, trust and anthropomorphism studies) and are stated before data collection; the paper explicitly notes hypotheses and the analysis plan were finalized one month prior to data collection. The qualitative analysis in Sec. 4.3.2 uses inductive thematic analysis, which is not a derivation. The acknowledged limitations in Sec. 6.3 (imagined rather than actual writing; character-by-character rather than token-by-token appearance) are threats to external validity or generalizability, not circularity. The skeptic's concern about repeated-measures data being analyzed as independent observations (df 4,2965) is a statistical correctness or robustness issue; it does not make any claimed result equivalent by construction to its inputs. Accordingly, no circular steps are present, and the appropriate score is 0.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

No fitted parameters or invented entities. The four speed anchors are hand-picked design choices; the axioms are standard statistical and domain assumptions for the experiment.

free parameters (4)
  • Slow speed anchor = 160 wpm
    Chosen via pilot tests as a slow but not absurdly slow presentation rate; not fitted to outcome data. Central comparisons depend on this anchor.
  • Medium speed anchor = 600 wpm
    Set slightly faster than average reading speed (200-400 wpm), chosen by hand based on prior VDU studies; not fitted.
  • Fast speed anchor = 6000 wpm
    Set to approximate real LLM generation speed (about 133 tokens/sec); chosen by hand.
  • Backwards/Random order at 600 wpm = 600 wpm, non-forward order
    Deliberately non-anthropomorphic presentation styles used as contrasts; not fitted.
assumptions (4)
  • domain assumption ANOVA is robust to Likert scale non-normality
    Norman (2010) is cited to justify parametric ANOVA on ordinal Likert responses; if this robustness claim is wrong, some significance levels may be inflated.
  • domain assumption Randomized speed-text pairing isolates presentation from content
    The key identification strategy assumes that random assignment of texts to speeds removes content confounds; the authors also check Flesch-Kincaid scores as a proxy.
  • domain assumption Genre systems frame captures meaningful differences between creative and professional writing
    The concept of genre systems (Berkenkotter, Bazerman) is assumed to provide a valid contrast for RQ2.
  • domain assumption Participants read along with the AI generation as the mechanism for comfort
    The qualitative finding that participants read along is used to explain comfort results; assumes self-reports reflect actual reading behavior.

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Cite this review

Pith. "Pith review of Thoughtful, Confused, or Untrustworthy: How Text Presentation Influences Perceptions of AI Writing Tools." pith.science (2026). https://pith.science/paper/R37QK2EY

@misc{pith2026250420365,
  author       = {Pith},
  title        = {Pith review of: Thoughtful, Confused, or Untrustworthy: How Text Presentation Influences Perceptions of AI Writing Tools},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/R37QK2EY}},
  note         = {Machine review of arXiv:2504.20365}
}
read the original abstract

AI writing tools have been shown to dramatically change the way people write, yet the effects of AI text presentation are not well understood nor always intentionally designed. Although text presentation in existing large language model interfaces is linked to the speed of the underlying model, text presentation speed can impact perceptions of AI systems, potentially influencing whether AI suggestions are accepted or rejected. In this paper, we analyze the effects of varying text generation speed in creative and professional writing scenarios on an online platform (n=297). We find that speed is correlated with perceived humanness and trustworthiness of the AI tool, as well as the perceived quality of the generated text. We discuss its implications on creative and writing processes, along with future steps in the intentional design of AI writing tool interfaces.

Figures

Figures reproduced from arXiv: 2504.20365 by the authors.

Figure 1
Figure 1. Text presentation style is a key design element of AI writing tools. This paper explores the possible impacts of five [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. This figure represents one example study order. For each participant, all marked conditions were randomized. [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Survey interface. The viewable text area dynamically resizes according to the width of the display. On mobile, users [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.