Design and Implementation of an AI-Based Korean Pronunciation Learning System with Personalized Feedback
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Objectives: The demand for Korean language learning has increased with global interest in Korean culture. However, non-native speakers struggle with pronunciation due to Korean's unique phonological features. This study aims to develop an AI-based pronunciation evaluation application to provide structured and personalized feedback. Methods: The system uses a client–server architecture, integrating an Android app, a FastAPI server, and an SQLite database. The AI model evaluates pronunciation based on accuracy, fluency, and completeness, and checks keyword usage in learners' spoken responses. Results: A pilot study with non-native Korean learners showed high user satisfaction with the system’s learning content and feedback quality. Conclusions: The AI-based application effectively supports Korean pronunciation learning by offering real-time, structured feedback. Pilot results suggest strong potential for broader application in Korean language education.
Abstract
Objectives: The demand for Korean language learning has increased with global interest in Korean culture. However, non-native speakers struggle with pronunciation due to Korean's unique phonological features. This study aims to develop an AI-based pronunciation evaluation application to provide structured and personalized feedback. Methods: The system uses a client–server architecture, integrating an Android app, a FastAPI server, and an SQLite database. The AI model evaluates pronunciation based on accuracy, fluency, and completeness, and checks keyword usage in learners' spoken responses. Results: A pilot study with non-native Korean learners showed high user satisfaction with the system’s learning content and feedback quality. Conclusions: The AI-based application effectively supports Korean pronunciation learning by offering real-time, structured feedback. Pilot results suggest strong potential for broader application in Korean language education.