AI-Based Adaptive Learning System in Arabic Language Education: Personalization of Materials and Enhancement of Learning Effectiveness

Abstract

This study explores the implementation of Artificial Intelligence (AI)-based adaptive learning systems in Arabic language education, focusing on content personalization and learning effectiveness. Adaptive learning, which employs intelligent algorithms to tailor learning paths based on individual student needs, represents a transformative approach to Arabic language instruction in the digital era. The research adopts a qualitative-descriptive methodology to analyze case studies from various AI-supported learning platforms integrated into Arabic instruction. Data were collected through document analysis, observation, and interviews with educators using adaptive systems such as ChatGPT, Duolingo Arabic, and custom LMS modules with AI analytics. Findings reveal that adaptive AI systems can significantly enhance learner engagement, vocabulary retention, and pronunciation accuracy by aligning content difficulty with learner proficiency. Furthermore, personalization encourages self-paced learning and supports diverse linguistic backgrounds, contributing to more inclusive Arabic education. However, the study also highlights challenges such as limited Arabic linguistic corpora for AI training, lack of teacher readiness, and ethical concerns regarding learner data privacy. The study concludes by proposing a hybrid pedagogical framework combining AI-driven adaptation with teacher-led contextual instruction to optimize learning outcomes.