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Reading the Readers Mind through Eye Tracking: Can AI Generated Texts Match Human Authors?

Publications: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

While Generative AI models like Large Language Models (LLMs)
are capable of generating extensive text, their efficacy in producing
readable content for human participants in experimental settings
remains to be evaluated. Further, eye-tracking technology is increas-
ingly utilized to study cognition and behavior, yet its application to
readers’ cognitive processes when exposed to AI-generated versus
human-authored texts remains unexplored. This study investigates
how text generated by LLMs influences reading by analyzing gaze
patterns.
The study collects gaze data from 13 participants as they read AI-
generated and human-authored passages. A comparative analysis is
conducted within subjects to assess gaze patterns between authors
and between text types based on the robust two-means clustering
(I2MC) algorithm to identify fixations. In addition, pupil dilation
and reading speed were examined.
Our findings reveal significant differences in fixation character-
istics not only between authors but also between AI-generated and
human-authored texts.
Original languageEnglish
Title of host publicationProceedings of the 2025 Symposium on Eye Tracking Research and Applications (ETRA '25)
Place of PublicationAssociation for Computing Machinery
Publication date2025
Pages1-7
Article number113
DOIs
Publication statusPublished - 2025
EventETRA: 2025 Symposium on Eye Tracking Research and Applications - Tokyo, Japan
Duration: 26 May 202529 May 2025
https://etra.acm.org/2025/

Conference

ConferenceETRA
Country/TerritoryJapan
CityTokyo
Period26/05/202529/05/2025
Internet address

Keywords

  • Eye-tracking
  • Fixation detection
  • AI and Human authored reading patterns

Artistic research

  • No

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