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

arXiv: https://arxiv.org/abs/2605.14999 · HTML: https://mlsanity.com/paper/2605.14999/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 62 references that are in the map.
- First posted on arXiv 14 May 2026.

## Builds on (most cited first, 15 of 15)

- [Students' Voices on Generative AI: Perceptions, Benefits, and Challenges in Higher Education](https://mlsanity.com/paper/2305.00290/) (2023)
- [Transparent AI Disclosure Obligations: Who, What, When, Where, Why, How](https://mlsanity.com/paper/2403.06823/) (2024)
- [Labeling Synthetic Content: User Perceptions of Warning Label Designs for AI-generated Content on Social Media](https://mlsanity.com/paper/2503.05711/) (2025)
- [The eyes know it: FakeET -- An Eye-tracking Database to Understand Deepfake Perception](https://mlsanity.com/paper/2006.06961/) (2020)
- [Factuality Checking in News Headlines with Eye Tracking](https://mlsanity.com/paper/2006.09736/) (2020)
- [Gaze-based Attention Recognition for Human-Robot Collaboration](https://mlsanity.com/paper/2303.17619/) (2023)
- [Quality Perceptions and Intended Engagement in Response to AI-Generated and AI-Assisted News](https://mlsanity.com/paper/2409.03500/) (2024)
- [Examining the Impact of Label Detail and Content Stakes on User Perceptions of AI-Generated Images on Social Media](https://mlsanity.com/paper/2510.19024/) (2025)
- [Understanding Trust Toward Human versus AI-generated Health Information through Behavioral and Physiological Sensing](https://mlsanity.com/paper/2512.12348/) (2025)
- [More Human or More AI? Visualizing Human-AI Collaboration Disclosures in Journalistic News Production](https://mlsanity.com/paper/2601.11072/) (2026)
- [Sensing What Surveys Miss: Understanding and Personalizing Proactive LLM Support by User Modeling](https://mlsanity.com/paper/2602.00880/) (2026)
- [True or false? Cognitive load when reading COVID-19 news headlines: an eye-tracking study](https://mlsanity.com/paper/2302.08597/) (2023)
- [PhysioCHI: Towards Best Practices for Integrating Physiological Signals in HCI](https://mlsanity.com/paper/2312.04223/) (2023)
- [Full Disclosure, Less Trust? How the Level of Detail about AI Use in News Writing Affects Readers' Trust](https://mlsanity.com/paper/2601.09620/) (2026)
- [Eyes Can't Always Tell: Fusing Eye Tracking and User Priors for User Modeling under AI Advice Conditions](https://mlsanity.com/paper/2604.01741/) (2026)

## Built on by (most cited first, 1 of 1)

- [Designed by Journalists, but Is It for Readers? Rethinking AI Disclosures and Transparency in News](https://mlsanity.com/paper/2606.11116/) (2026)

## Abstract (arXiv)

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 3×2×2 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.