An AI-Based Model of Maharah Qira’ah Instruction: Integrating Linguistic Analysis and Islamic Pedagogy for Deep Meaning Making

Abstract

The rapid development of Artificial Intelligence (AI) has transformed Arabic language education by enabling automated linguistic analysis, speech-to-text processing, and real-time feedback. In the context of maharah qira’ah, existing instruction often emphasizes phonetic decoding and literal translation, while deeper meaning-making remains limited. This study aims to develop a conceptual AI-based model that integrates linguistic analytics with Islamic pedagogy to promote reflective and meaningful reading. Using descriptive qualitative library research, the study synthesizes literature from 2015–2025 and analyzes data through thematic mapping and content analysis. Findings indicate that Arabic NLP tools such as AraBERT, CAMeL Tools, Farasa, and Lisan, alongside modern speech recognition systems like Whisper, effectively enhance phonetic accuracy, error detection, and semantic awareness. When paired with guided interpretation and reflective learning, AI serves as an epistemic partner supporting qira’ah ma‘nawiyyah. The proposed model offers implications for curriculum innovation, AI-based assessment, and strengthening the lecturer’s role as a murabbi in AI-augmented Arabic reading instruction.