Analyzing Phonetic Errors Among Non-Native Arabic Learners Through Artificial Intelligence

Abstract

Accurate pronunciation is essential for non-native Arabic learners, yet many face persistent difficulties in articulating specific sounds, leading to phonetic errors that impede comprehension. With technological advancements, artificial intelligence (AI)—particularly Automatic Speech Recognition (ASR)—offers promising solutions for identifying and correcting such errors. This study explores the application of AI in analyzing phonetic inaccuracies among Arabic learners through an experimental model using ASR technology. Employing a descriptive-analytical approach, ten undergraduate students from Universitas Islam Negeri Sumatera Utara were asked to read short Arabic passages. Their recordings were processed through ASR to detect pronunciation errors, including sound substitution, weak articulation, and inaccuracies in elongation (madd) and nasalization (ghunnah). The results demonstrate that ASR effectively identifies recurring phonetic error patterns, providing instructors with valuable diagnostic insights and enabling timely, personalized feedback. The study concludes that integrating AI-driven pronunciation analysis into Arabic language instruction can significantly enhance learning outcomes, support autonomous practice, and promote more accurate oral proficiency. It recommends that educators and institutions adopt ASR-based tools as part of digital language pedagogy to strengthen pronunciation training and foster continuous learner engagement.