Regional Adaptation Mechanisms of AI-Empowered International Chinese Language Education in Malaysia: A Qualitative Study of Technology–Culture–Pedagogy Integration

Abstract

Artificial intelligence (AI) has reshaped language education globally, yet its impact on international Chinese language education remains uneven across cultural contexts. In multilingual Malaysia, AI-supported Chinese language teaching cannot rely on direct technological transfer but requires regional adaptation that aligns AI affordances with local linguistic ecology, cultural norms, and pedagogical practices. This study explores the regional adaptation mechanisms of AI-empowered international Chinese language education in Malaysia, focusing on how teachers and institutions localize AI tools such as speech recognition, automated feedback, intelligent assessment, and generative learning platforms.Using a qualitative design, the study triangulates policy documents, classroom observations, and semi-structured interviews with teachers, learners, and educational stakeholders. Thematic analysis identifies three interconnected mechanisms: (1) institutional-policy alignment, shaping adoption through governance, resource allocation, and localized evaluation standards; (2) pedagogical orchestration, emphasizing teachers’ mediation in task redesign, feedback calibration, and human–AI role negotiation; and (3) cultural-linguistic calibration, addressing code-switching practices, identity sensitivities, and culturally responsive content. Building on these findings, this study proposes a staged model (introduction–alignment–integration–optimization) to explain how AI achieves legitimacy and sustainability in Malaysia’s Chinese language education ecosystem. It offers an ASEAN-oriented account of AI localization and provides practical implications for teacher training, platform development, and China–Malaysia educational cooperation.