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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Mohammed Jebbari
Hassan II University of Casablanca
Computer Science & IT Engineering

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Hybrid convolutional neural network-bidirectional long short-term memory model for Arabic sentence readability assessment Mohamed Amine Ouassil; Mohammed Jebbari; Rabia Rachidi; Mouaad Errami; Soufiane Hamida; Bouchaib Cherradi; Abdelhadi Raihani
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2849-2862

Abstract

In the current educational landscape, a large number of educators prefer using generative artificial intelligence techniques to produce textual content to be presented for learning. However, these generated texts may not meet the specific needs of learners or align with their abilities. Many traditional methods and techniques can be employed to assess the complexity of a text, such as traditional readability formulas, but these techniques are time consuming and labor-intensive. In this paper, we introduce a deep learning approach for automatically evaluating the readability of Arabic texts by analyzing and classifying sentences into different difficulty levels within educational content. The initial stage of the proposed approach is preprocessing textual content and leveraging natural language processing (NLP) methodologies for feature extraction, such as Word2Vec. The approach then concentrates on refining and evaluating a deep learning model to classify text into different readability levels. This paper introduces a hybrid classification model that combines convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM) layers, attaining an accuracy of 96.68%. This model demonstrates the significance of applying hybrid deep learning models in analyzing educational materials and establishes a foundation for subsequent progress in the field of automated Arabic readability assessment.
Co-Authors Abdelhadi Raihani Bouchaib Cherradi Mohamed Amine Ouassil Mouaad Errami Rabia Rachidi Soufiane Hamida