THE EFFECT OF AI-ASSISTED SPEAKING PRACTICE ON STUDENTS’ PRONUNCIATION ACCURACY

Abstract

this paper synthesizes empirical research on the efficacy of Artificial Intelligence (AI)-assisted speaking practice, particularly tools leveraging Automatic Speech Recognition (ASR), in improving the pronunciation accuracy of language learners. The analysis reveals a significant positive correlation between the use of AI pronunciation tools and measurable gains in learners' phonological skills. The primary mechanism driving this improvement is the technology's ability to provide instantaneous, personalized, and explicit corrective feedback at the segmental (phoneme) level—an affordance often constrained in traditional classroom settings by time and class size. Furthermore, AI tools foster a low-anxiety, non-judgmental practice environment, which enhances learner motivation, confidence, and autonomy. However, the effectiveness of these tools is not uniform. A critical review of the literature indicates that AI is substantially more effective at correcting segmental errors than suprasegmental features like intonation and stress, which are crucial for natural communication. Moreover, challenges such as technological limitations in speech recognition accuracy, the lack of contextual and pragmatic feedback, and the risk of learner over-reliance necessitate a carefully scaffolded pedagogical approach. The paper concludes that while AI-assisted tools are powerful supplements for pronunciation instruction, they cannot wholly replace the nuanced role of a human teacher. Their optimal use lies in a blended learning model where the teacher's role evolves from a primary feedback provider to a facilitator who guides students in critically interpreting AI feedback and integrates technology-based practice with holistic, communicative classroom activities.