AraEyebility: Eye-Tracking Data for Arabic Text Readability

Assessing text readability is important for helping language learners and readers select texts that match their proficiency levels. Research in cognitive psychology, which uses behavioral data such as eye-tracking and electroencephalogram signals, has shown its effectiveness in detecting cognitive activities that correlate with text difficulty during reading. However, Arabic, with its distinctive linguistic characteristics, presents unique challenges in readability assessment using cognitive data. While behavioral data have been employed in readability assessments, their full potential, particularly in Arabic contexts, remains underexplored. This paper presents the development of the first Arabic eye-tracking corpus, comprising eye movement data collected from Arabic-speaking participants, with a total of 57,617 words. Subsequently, this corpus can be utilized to evaluate a broad spectrum of text-based and gaze-based features, employing machine learning and deep learning methods to improve Arabic readability assessments by integrating cognitive data into the readability assessment process.