Bridging the Pedago-Technical Gap: A Critical Evaluation of AI-Powered Speech Recognition in Sacred Language Acquisition

Abstract

The global landscape of education has undergone a profound digital transformation, yet the application of Artificial Intelligence (AI) in transmitting sacred knowledge faces unique pedagogical and phonetic challenges. The Qur'an is preserved through a deeply rooted oral tradition known as talaqqī, requiring precise face-to-face transmission to master tajwīd (articulation and intonation rules). While a new wave of AI-powered applications promises real-time pronunciation feedback via Automatic Speech Recognition (ASR), significant concerns exist regarding their linguistic accuracy, handling of diverse accents, and fundamental pedagogical alignment. This paper presents a heuristic evaluation and systematic review of three leading digital Qur'an platforms (Tarteel, Ayat, and Quran.com) against a synthesized theoretical framework combining Western learning sciences with traditional Islamic pedagogy. The findings reveal a critical "pedago-technical gap," characterized by binary feedback mechanisms, accent biases, and the decontextualization of sacred learning. The study proposes a comprehensive enhancement framework prioritizing accent-inclusive ASR, teacher-linked supervision, and actionable phonetic feedback to ensure technology serves as a faithful supplement to traditional oral reception.