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All Journal ATHLA: Journal of Arabic Teaching, Linguistic, And Literature
Abdul Ghani bin Md Din
Universiti Islam Antarabangsa Sultan Abdul Halim Mu'adzam Shah
Religion Education Languange, Linguistic, Communication & Media

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From CEFR to AI-Assisted Learning: Contemporary Transformations in Arabic Language Teaching for Non-Native Speakers Muh. Sabilar Rosyad; Muhammad Afthon Ulin Nuha; Muhammad Hafidz; Muhammad Abdur Rozaq; Ahmad Nuruddin; Abdul Ghani bin Md Din
ATHLA : Journal of Arabic Teaching, Linguistic and Literature Vol. 7 No. 1 (2026)
Publisher : UIN Raden Mas Said Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22515/athla.v7i1.14295

Abstract

The rapid advancement of artificial intelligence (AI) has significantly reshaped language education, raising critical questions about the balance between pedagogical personalization and standardized proficiency frameworks. In Arabic language teaching for non-native speakers, this tension is particularly evident due to the linguistic complexity of Arabic and its diverse instructional contexts. This article aims to critically examine how CEFR-based standardization and AI-assisted learning can be coherently integrated to enhance Arabic language pedagogy without undermining curricular coherence or cultural specificity. Employing a qualitative, thematic literature analysis, this study synthesizes findings from recent research on CEFR implementation, AI-supported language learning, and contemporary pedagogical approaches in Arabic education. The results reveal three major themes: the role of CEFR in standardizing Arabic language curricula, the potential of AI to enable pedagogical personalization, and the need for an integrated framework that aligns technological innovation with pedagogical and ethical considerations. Building on these findings, the study proposes an integrated CEFR-AI pedagogical framework in which CEFR functions as a macro-level curricular backbone, pedagogy mediates contextual and cultural relevance, and AI operationalizes adaptive learning and formative assessment. The discussion highlights the theoretical implications of reconciling standardization and personalization, positioning AI as a pedagogically subordinate yet transformative tool. This research contributes to applied linguistics by advancing a scalable, context-sensitive model for Arabic language teaching and offers directions for future empirical research on AI-enhanced, standards-aligned language education.
Co-Authors Ahmad Nuruddin Muh. Sabilar Rosyad Muhammad Abdur Rozaq Muhammad Afthon Ulin Nuha Muhammad Hafidz