Artificial Intelligence in Teaching Mahārah al-Qirā’ah A Conceptual Study of Digital Texts, Annotation Tools, and Personalized Reading
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Abstract
This conceptual study examines how artificial intelligence (AI) can be harnessed to support the teaching of mahārah al-qirā’ah (Arabic reading skill) among non-native learners, whose progress is frequently constrained by the language’s root-and-pattern morphology, unfamiliar script, and the customary absence of short-vowel diacritics in authentic texts. Rather than reporting empirical data, the study adopts an integrative literature review approach, thematically synthesising 28 sources retrieved from Scopus, ERIC, and Google Scholar. Three interrelated AI-driven affordances are identified: AI-generated and AI-adapted digital texts that calibrate lexical, morphological, and diacritical complexity; intelligent annotation tools that provide real-time root-pattern glossing and morphological parsing; and personalized reading-pathway systems that model learner proficiency and adapt content sequencing. These affordances are synthesised into a proposed AI-Mediated Mahārah al-Qirā’ah Model, grounded in schema theory, the interactive-compensatory model of reading, and the technology acceptance model. The framework offers curriculum developers, EdTech designers, and Arabic instructors a conceptual foundation for designing AI-supported reading instruction, while underscoring the need for empirical validation and dialect-inclusive AI models attentive to the realities of non-native, particularly Indonesian, learners.
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