Interactive Knowledge Base for Designing an AI-based Chinese Computer-assisted Pronunciation Training: A Systematic Review

Spoken Chinese proficiency remains significantly lower than listening and reading skills among Chinese learners worldwide. While Automatic Intelligence (AI)-based Computer-Assisted Pronunciation Training (CAPT) systems support English learning effectively, their application in Chinese language learning is limited. Prioritizing technology over pedagogy and lack grounding in Second Language Acquisition (SLA) theory have led to the tension between technology and pedagogy. This study aimed to identify evidence-based, pedagogically informed design guidelines for an AI-driven Chinese CAPT system for non-native learners. Grounded in Interactive Hypothesis (IH) of SLA and Richey and Klein’s Design and Development Research (DDR) framework, the study first constructed a theoretical knowledge base. Then, a systematic literature review was conducted following the PRISMA flowchart, focusing on studies related to Oral Corrective Feedback (OCF) in the context of Chinese language learning. Six key themes and corresponding pedagogical design guidelines were identified, offering theoretical and empirical support for CAPT system design. Research limitations and contributions were also discussed.