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arxiv: 2605.14999 · v1 · pith:VAL5X2P5new · submitted 2026-05-14 · 💻 cs.HC · cs.AI· cs.CY

Towards Gaze-Informed AI Disclosure Interfaces: Eye-Tracking Attentional and Cognitive Load While Reading AI-Assisted News

Pooja Prajod , Hannes Cools , Thomas R\"oggla , Pablo Cesar , Abdallah El Ali This is my paper

Pith reviewed 2026-06-30 19:58 UTC · model grok-4.3

classification 💻 cs.HC cs.AIcs.CY
keywords AI disclosureeye-trackingattentional loadcognitive loadnews readinggenerative AIhuman-AI interactiondisclosure design
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The pith

One-line AI disclosures in news increase readers' fixation durations and saccade counts without raising cognitive load.

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

The study tests three levels of AI-use disclosure in news articles while tracking eye movements and self-reported effort. One-line labels produced more visual scanning than either no label or full details, especially when AI had edited the text. NASA-TLX scores and pupil size remained flat across all conditions, so the added scrutiny did not translate into measurable cognitive burden. The authors link the pattern to Information-Gap Theory and recommend disclosure interfaces that can adapt based on observed gaze.

Core claim

In a 3×2×2 mixed factorial experiment, one-line AI disclosures produced reliably higher fixation durations and saccade counts than the no-disclosure or detailed-disclosure conditions, with the effect strongest for AI-edited stories; detailed disclosures added no further visual cost. Neither NASA-TLX ratings nor pupil-diameter measures differed across disclosure levels, indicating that disclosure detail affects attentional allocation but not cognitive load.

What carries the argument

Eye-tracking measures (fixation duration, saccade count) contrasted with NASA-TLX and pupil diameter to separate attentional from cognitive load during reading of AI-assisted news under varying disclosure detail.

If this is right

  • Detailed disclosures avoid the extra visual scrutiny triggered by one-line labels.
  • AI-use disclosures impose no detectable cognitive burden at any tested level of detail.
  • Readers express preference for detailed or on-demand disclosure formats in follow-up interviews.
  • Gaze patterns can serve as a signal for adaptive disclosure interfaces that change transparency level.

Where Pith is reading between the lines

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

  • Interfaces could monitor gaze in real time and expand a brief label into full text when fixations rise.
  • The attentional-cost finding may generalize to other short-form AI labels such as social-media or product summaries.
  • Field studies outside the lab would be needed to check whether the same gaze patterns appear during ordinary news consumption.

Load-bearing premise

That the chosen eye-tracking metrics and NASA-TLX scores isolate the effect of disclosure detail rather than differences in story interest or reading strategy.

What would settle it

A replication in which participants read the same articles on their own devices and show no elevation in fixation duration or saccades for one-line disclosures compared with no disclosure.

Figures

Figures reproduced from arXiv: 2605.14999 by Abdallah El Ali, Hannes Cools, Pablo Cesar, Pooja Prajod, Thomas R\"oggla.

Figure 1
Figure 1. Figure 1: Illustration of key findings: one-line AI-use disclosures increase readers’ attentional load (larger, more frequent [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the experimental protocol. The eye tracker was calibrated during preparation and recorded gaze data [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Grouped bar visualization of (a) mean NASA-TLX workload scores, (b) mean pupil diameter, (c) mean total fixation [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
read the original abstract

As generative AI becomes increasingly integrated into journalism, designing effective AI-use disclosures that inform readers without imposing unnecessary burden is a key challenge. While prior research has primarily focused on trust and credibility, the impact of disclosures on readers' attentional and cognitive load remains underexplored. To address this gap, we conducted a $3\times2\times2$ mixed factorial study manipulating the level of AI-use disclosure detail (none, one-line, detailed), news type (politics, lifestyle), and role of AI (editing, partial content generation), measuring load via NASA-TLX and eye-tracking. Our results reveal a significant attentional cost: one-line disclosures resulted in significantly higher fixation durations and saccade counts, particularly for AI-edited content. Detailed disclosures did not impose additional burden. Drawing on Information-Gap Theory, we argue that brief labels may trigger increased visual scrutiny by alerting readers to AI use without providing enough information. NASA-TLX scores and pupil diameter showed no significant differences across conditions, suggesting that AI-use disclosures do not impose cognitive burden regardless of the detail level. Interview insights contextualize these findings and reveal a strong preference for detailed or ``detail-on-demand'' designs. Our findings inform the design of gaze-informed adaptive disclosure interfaces that dynamically adjust transparency levels based on readers' attentional patterns and news context.

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 3×2×2 mixed factorial study on attentional and cognitive load during reading of AI-assisted news. Factors are disclosure detail (none, one-line, detailed), news type (politics, lifestyle), and AI role (editing vs. partial content generation). Load is measured via eye-tracking (fixation duration, saccade count, pupil diameter) plus NASA-TLX, supplemented by interviews. Central claim: one-line disclosures produce significantly higher fixation durations and saccade counts (especially for AI-edited content) while detailed disclosures do not; NASA-TLX and pupil measures show no differences, interpreted as attentional scrutiny without cognitive burden per Information-Gap Theory. The work advocates gaze-informed adaptive disclosure interfaces.

Significance. If the eye-movement effects survive controls for stimulus properties, the multi-method design (eye-tracking + subjective scales + qualitative data) offers useful evidence for disclosure design in AI journalism. The null cognitive-load findings and preference for detailed or on-demand formats could inform practical interface guidelines. The study is empirical rather than theoretical and does not include machine-checked proofs or parameter-free derivations.

major comments (2)
  1. [Methods] Methods (stimulus materials subsection): No report that articles were matched or covaried for length, sentence complexity, lexical difficulty, or readability metrics (e.g., Flesch-Kincaid) across disclosure conditions. This is load-bearing for the central claim because the reported increases in fixation duration and saccade count for one-line disclosures could be driven by bottom-up text features rather than the disclosure manipulation.
  2. [Results] Results (and abstract): Statistically significant differences are asserted for fixation/saccade metrics, yet sample size (N), exact tests (e.g., mixed ANOVA details, post-hoc corrections), effect sizes, and data-exclusion criteria are not provided. These omissions prevent evaluation of the reliability of both the positive attentional-cost finding and the null results on NASA-TLX and pupil diameter.
minor comments (2)
  1. [Abstract] Abstract: Could state the total participant count and clarify which factors are within- vs. between-subjects to aid immediate assessment of design power.
  2. [Discussion] Discussion: The appeal to Information-Gap Theory would be strengthened by a one-sentence summary of the theory's relevant predictions and a supporting citation.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. The two major comments identify important gaps in methodological transparency and statistical reporting that we will address in revision.

read point-by-point responses

  1. Referee: [Methods] Methods (stimulus materials subsection): No report that articles were matched or covaried for length, sentence complexity, lexical difficulty, or readability metrics (e.g., Flesch-Kincaid) across disclosure conditions. This is load-bearing for the central claim because the reported increases in fixation duration and saccade count for one-line disclosures could be driven by bottom-up text features rather than the disclosure manipulation.

    Authors: We agree this detail is necessary to rule out stimulus confounds. Articles were drawn from the same news outlets and selected to be comparable in approximate length and topic, but formal readability metrics and covariate reporting were omitted. In the revised manuscript we will add a table or paragraph reporting mean article length, sentence count, and Flesch-Kincaid scores per condition, and we will re-run the primary analyses with these metrics as covariates where appropriate. revision: yes

  2. Referee: [Results] Results (and abstract): Statistically significant differences are asserted for fixation/saccade metrics, yet sample size (N), exact tests (e.g., mixed ANOVA details, post-hoc corrections), effect sizes, and data-exclusion criteria are not provided. These omissions prevent evaluation of the reliability of both the positive attentional-cost finding and the null results on NASA-TLX and pupil diameter.

    Authors: We accept that the current version lacks sufficient statistical detail. The revised Results section will report the final sample size after exclusions, the precise mixed ANOVA models (including any sphericity corrections), post-hoc procedures and adjustments, effect sizes for all reported effects, and the full data-exclusion criteria. We will also ensure the abstract references the key statistical outcomes. revision: yes

Circularity Check

0 steps flagged

Empirical measurement study with no derivations or self-referential reductions

full rationale

The paper describes a 3×2×2 mixed factorial experiment collecting eye-tracking metrics (fixation duration, saccade count, pupil diameter) and NASA-TLX scores, then reports direct statistical comparisons across disclosure levels, news types, and AI roles. No equations, fitted parameters, predictions derived from inputs, or load-bearing self-citations appear in the abstract or design. Results are presented as observations from the collected data rather than any derivation chain that reduces to its own inputs by construction. The Information-Gap Theory reference is interpretive framing, not a mathematical premise. This is a standard empirical HCI study whose central claims rest on experimental measurements, not on any of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The work rests on standard assumptions of eye-tracking validity and survey psychometrics plus conventional experimental design; no free parameters, new entities, or ad-hoc axioms are introduced beyond those implicit in the measurement tools.

axioms (2)
  • domain assumption Eye-tracking metrics (fixation duration, saccade count) and NASA-TLX validly index attentional and cognitive load in news reading tasks
    Invoked when interpreting the significant differences and null results as evidence about load.
  • standard math Standard statistical assumptions hold for the 3x2x2 mixed factorial analysis
    Required to interpret reported significance of attentional effects.

pith-pipeline@v0.9.1-grok · 5789 in / 1348 out tokens · 27749 ms · 2026-06-30T19:58:42.041386+00:00 · methodology

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Designed by Journalists, but Is It for Readers? Rethinking AI Disclosures and Transparency in News

    cs.CY 2026-06 unverdicted novelty 5.0

    User study finds detailed AI disclosures in news reduce trust and one-line labels leave gaps, with readers proposing agency-focused alternatives like detail-on-demand and proportional visualizations.

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