Developing an AI-Assisted Arabic Language Learning Model to Improve Students’ Vocabulary and Communicative Competence

Abstract

Artificial intelligence (AI) offers new opportunities for creating personalized, interactive, and adaptive environments for Arabic language learning. However, the integration of AI into Arabic instruction requires a structured pedagogical model that simultaneously addresses vocabulary acquisition and communicative competence. This study aimed to develop and evaluate an AI-assisted Arabic language learning model designed to improve students’ vocabulary and communicative competence. The study employed a Research and Development (R&D) approach consisting of needs analysis, model design, prototype development, expert validation, limited trial, revision, and effectiveness testing. The participants included 40 Arabic language students, 20 students in the limited trial, and five experts in Arabic language education, instructional design, and educational technology. Data were collected through needs-analysis questionnaires, expert-validation instruments, vocabulary tests, communicative competence assessments, and student-response questionnaires. Quantitative data were analyzed using descriptive statistics, paired-samples t-tests, normalized gain, and Cohen’s d, while qualitative feedback was analyzed descriptively. The results showed that the developed model achieved very high validity (89.1%) and practicality (88.0%). Vocabulary scores increased from a mean of 60.30 to 73.18, while communicative competence increased from 59.73 to 73.49. Both improvements were statistically significant (p < .001), with very large effect sizes. The model therefore provides a practical AI-supported framework that integrates personalized vocabulary practice, conversational simulation, immediate feedback, and teacher supervision to strengthen Arabic language learning.