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

doi:10.22515/athla.v7i1.14295

Authors

Muh. Sabilar Rosyad UIN Sunan Kalijaga Yogyakarta
Muhammad Afthon Ulin Nuha UIN Sayyid Ali Rahmatullah Tulungagung
Muhammad Hafidz Universitas K.H Abdul Chalim Pacet Mojokerto
Muhammad Abdur Rozaq Universitas Islam Tribakti Lirboyo Kediri
Ahmad Nuruddin Institut Agama Islam Sunan Kalijogo Malang
Abdul Ghani bin Md Din Universiti Islam Antarabangsa Sultan Abdul Halim Mu'adzam Shah

DOI:

https://doi.org/10.22515/athla.v7i1.14295

Keywords:

Arabic Language Teaching, Artificial Intelligence in Language Education, Pedagogical Personalization, Curriculum Standardization, Integrated CEFR–AI Framework

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.

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