Using physiological data helps to identify the cognitive processing in the human brain. One method of obtaining these behavioral signals is by using eye-tracking technology. Previous cognitive psychology literature shows that readable and difficult-to-read texts are associated with certain eye movement patterns, which has recently encouraged researchers to use these patterns for readability assessment tasks. However, although it seems promising, this research direction has not been explored adequately, particularly for Arabic. The Arabic language is defined by its own rules and has its own characteristics and challenges. There is still a clear gap in determining the potential of using eye-tracking measures to improve Arabic text. Motivated by this, we present a pilot study to explore the extent to which eye-tracking measures enhance Arabic text readability. We collected the eye movements of 41 participants while reading Arabic texts to provide real-time processing of the text; these data were further analyzed and used to build several readability prediction models using different regression algorithms. The findings show an improvement in the readability prediction task, which requires further investigation. To the best of our knowledge, this work is the first study to explore the relationship between Arabic readability and eye movement patterns.

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.

Evaluating text readability is crucial for supporting both language learners and native readers in selecting appropriate materials. Cognitive psychology research, leveraging behavioral data such as eye-tracking and electroencephalogram (EEG) signals, has demonstrated effectiveness in identifying cognitive activities associated with text difficulty during reading. However, the distinctive linguistic characteristics of Arabic present unique challenges for applying such data in readability assessments. While behavioral signals have been explored for this purpose, their potential for Arabic remains underutilized. This study aims to advance Arabic readability assessments by integrating eye-tracking features into computational models. It presents a series of experiments that utilize both text-based and gaze-based features within machine learning (ML) and deep learning (DL) frameworks. The gaze-based features were extracted from the AraEyebility corpus, which contains eye-tracking data collected from 15 native Arabic speakers. The experimental results show that ensemble ML models, particularly AdaBoost with linguistic and eye-tracking handcrafted features, outperform ML models using TF-IDF and DL models employing word embedding vectorization. Among the DL models, convolutional neural networks (CNNs) achieved the best performance with combined linguistic and eye-tracking features. These findings underscore the value of cognitive data and emphasize the need for exploration to fully realize its potential in Arabic readability assessment.

Manal Alhassoun, Imaan Mohammed Alkhanen, Nouf Alshalawi, Ibtehal Baazeem, Waleed Alsanie. Proceedings of The Third Arabic Natural Language Processing Conference. 2025.

Background: Composite materials are not easy to model using traditional experiments and physics-based approaches due to their diverse properties and multiscale nature. This review summarizes how artificial intelligence (AI) methodologies, including machine learning, deep learning, and generative AI, have been increasingly transforming and how they have been increasingly applied to composite materials research. It combines their possible use across the prediction of their properties, damage detection, structural health monitoring, and materials design. Methods: Searches were performed in Scopus and Google Scholar for articles published using combined keywords such as “(artificial intelligence OR machine learning OR deep learning OR Generative AI) AND (composite OR fiber reinforced) AND (prediction OR optimization OR characterization)”. The search strategy was designed to capture both fundamental developments in AI methodologies relevant to materials research and their specific applications to composite materials. The identified publications underwent a two-step selection process: (i) an initial review of titles and abstracts, and (ii) a detailed full-text evaluation. Studies were included if they provided explicit descriptions of AI models, clearly specified datasets, and applied quantitative performance assessments to composite materials-related applications such as material property prediction, damage detection, characterization, or manufacturing process optimization. For each selected article, relevant information was extracted in a structured and consistent manner, including the type of composite material, AI methodology, input parameters, dataset characteristics and size, validation methods, and application domain. These data were used to support both quantitative trend analysis and the qualitative identification of research gaps and future research opportunities in composite materials research. Results: Supervised learning methods, in particular artificial neural networks, support vector machines, random forests, and decision trees, were commonly used and exhibited durable potential accuracy in predicting properties of composite materials. Unsupervised methods such as principal component analysis (PCA) and clustering were comparatively underused. Generative AI, including generative adversarial networks (GANs) and variational autoencoders (VAEs), and physics-informed models emerged as growing approaches for synthetic data generation and improved interpretability, respectively. Limitations: The reviewed studies were frequently limited by small or heterogeneous datasets, weak generalizability testing beyond training conditions, high computational requirements and limited model interpretability (“black box” behavior); a risk-of-bias assessment across included studies was not formally conducted. Conclusions: AI is shifting composite materials research from trial-and-error toward data-driven, predictive design. Realizing its full potential will require open, well-annotated datasets, physics-aware and explainable models, and closed-loop, active-learning workflows linking prediction to experimental validation.