Systematic Review on The Role of AI Self-Directed Learning in Enhancing Arabic Language Skill Acquisition

Abstract

This conceptual paper reviews the role of AI-powered self-directed learning (SDL) tools, with a focus on adaptive learning platforms, in Arabic skill acquisition. Despite widespread adoption of AI in language learning, there is a persistent gap in rigorous and up-to-date syntheses on how AI-enabled SDL supports Arabic learning processes within diverse linguistics features and translate these insights into a coherent framework that guides design, implementation and evaluation in real-world contexts. Studying on a broad spectrum of theoretical perspectives, this paper is intended to synthesize how organised practices and adaptive learning platforms can scaffold learners’ comprehension in all language aspects. It presents a structured synthesis of concepts, identify recurrent gaps in theory and evidence, and propose a multi-layer framework that positions AI-driven features, learner factors and instructional design within an input–process–outcome model. By utilising qualitative research methodology, this systematic review examines the most relevant 37 research articles to the role of artificial intelligence (AI)-based self-directed learning applications in the acquisition of Arabic language skills in the contemporary educational context. Implications are offered for researchers, educators, and developers aiming to design culturally and linguistically relevant AI language skill tools for Arabic. This analysis found, despite these achievements, there are challenges that need to be addressed for some complex tasks due to language’s deep cultural context and variation of dialects and expressions The paper also outlines a rigorous agenda for future research, including methodological approaches to validate the framework across dialects, scripts, and learner contexts.