Deep Learning as An Innovative Approach in Arabic Curriculum Development

Irfan Munir, Mohamad Sarip, Nurhidayati Nurhidayati, Helmy Ahmad Rabbany, Naura Nazhifah

Abstract


The rapid growth of artificial intelligence in the digital era has encouraged the need for more adaptive and technology-integrated curriculum designs, including in Arabic language education. Conventional curricula are often less responsive to learners’ needs, particularly in personalization, interaction, and data-driven evaluation. This study aims to explore the potential of deep learning technology as an innovative approach in the development of the Arabic language curriculum. Using a qualitative approach with a library research method, data were collected through systematic searches of reputable literature from indexed journals, academic books, and policy documents published between 2016 and 2024. The data were analyzed using content analysis to identify thematic patterns, classify relevant concepts, and synthesize findings from previous studies. The results indicate that the integration of deep learning supports the creation of an adaptive, contextual, and responsive curriculum by identifying students’ learning patterns, providing individualized material recommendations, and assisting teachers in instructional decision-making. This article proposes a deep learning-based Arabic language curriculum model consisting of three stages: input, process, and output, while also discussing key implementation challenges and strategic solutions. The findings are expected to contribute theoretically and practically to policymakers, curriculum developers, and educators in building a more modern and sustainable Arabic learning system.


Keywords


Adaptive Learning; Arabic Language; Curriculum; Deep Learning

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DOI: https://doi.org/10.15548/lisaanuna.v8i2.11948

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