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

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 increasingly 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 characteristics not only between authors but also between AI-generated and human-authored texts.