Integrating Multimodal Artificial Intelligence in Qur'anic Education: Architecture, Technical Precision, and Pedagogical Efficacy

Le résumé fourni par la source

Objective: This research systematically investigates the integration of Artificial Intelligence (AI) to facilitate personalized learning pathways in Qur'anic education, focusing on phonetic accuracy (tajwid) and student engagement. The study aims to bridge the gap between traditional instruction and modern "precision pedagogy" through a synthesis of multidisciplinary evidence. Methodology: Adhering to the PRISMA protocol, a Systematic Literature Review (SLR) was conducted on 25 high-impact, peer-reviewed studies. The analysis utilized the Input-Process-Output (IPO) framework to classify AI applications, ranging from Large Language Models (LLMs) such as ChatGPT to Deep Learning algorithms like CNN and U-Net, adapted from precision medical technologies. Findings: The literature synthesis reveals that AI-assisted tactical instruction significantly outperforms traditional methods in improving phonetic precision and reducing "speech anxiety." Intelligent Tutoring Systems (ITS) integrated with LLMs provide measurable, instantaneous feedback, while the anthropomorphic features of AI assistants enhance learning motivation. However, technical effectiveness is mediated by the Uncanny Valley effect and the digital literacy of religious educators. Implications: The results suggest that "Precision Pedagogy" can standardize Qur'anic learning globally without neglecting individual cognitive needs. Policymakers and Islamic institutions should prioritize AI ethical frameworks and data privacy, utilizing synthetic data to protect students' biometric voice identities. Originality: This study is among the first to adapt concepts from precision oncology and multi-omic integration into Islamic Education, proposing a novel "Qur'anic ITS" architectural model that balances technical precision with ethical-spiritual sustainability.

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