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

2026-05-14

AI Summary

This study addresses how to effectively inform users about AI involvement while minimizing attentional and cognitive burden. Employing a 3×2×2 mixed experimental design, the research systematically evaluates the impact of different AI disclosure strategies through eye-tracking, NASA-TLX cognitive load assessments, pupil diameter measurements, and semi-structured interviews. Findings indicate that brief, one-line labels significantly increase visual fixation duration and saccade frequency, whereas detailed disclosures do not impose additional cognitive load. Participants expressed a clear preference for either detailed or on-demand disclosure formats. Grounded in the information gap theory, this work proposes an adaptive “on-demand elaboration” interface design that dynamically tailors disclosure depth to user needs, offering a novel approach to balancing transparency with user experience in human-AI interaction.

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.

Problem

• AI disclosure
• attentional load
• cognitive load
• eye-tracking
• generative AI

Innovation

• gaze-informed interfaces
• eye-tracking
• AI disclosure
• attentional load
• adaptive transparency