Content Knowledge Base for AI-based Chinese Computer-assisted Pronunciation Training
Abstract
Integrating the state-of-the-art Automatic Technology (AI) technology into the Computer-Assisted Pronunciation Training (CAPT) system to assist language acquisition could address the various drawbacks of conventional pronunciation and enhance its effectiveness. However, little attention has been given to their application in Chinese language acquisition, and the lack of guidelines grounded in Second Language Acquisition (SLA) theories and pronunciation pedagogy has led to the tension between technology and pedagogy, limited the understanding of their application beyond English, and hindered their broader application. Grounded in the knowledge framework of AI-based Chinese CAPT system based on the Richey and Klein’s (2007) Design and Development Research (DDR) framework, the Monitor Theory and the Speech Acquisition Model (SAM), this study aimed to establish the content knowledge base informed by Chinese pronunciation pedagogy and derive evidence-based, pedagogically sound design guidelines for the AI-based Chinese CAPT system for non-native Chinese learners. Alongside the guidelines previously derived from the SLA Interactive theories and Oral Corrective Feedback (OCF) research in the Chinese setting, these could inform future design and development of the system. Employing the qualitative method of thematic analysis, three key themes were identified from a literature review on Chinese pronunciation acquisition, leading to the formulation of pedagogical design guidelines. These findings offer valuable insights into Chinese pronunciation practices and learners’ needs, providing a robust theoretical and empirical foundation for future design and development research. Finally, the study discussed its limitations and contributions.
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References
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