AIGC-driven transformation of international Chinese language education in Kyrgyzstan

This study investigated the integration of Artificial Intelligence Generated Content technologies with Second Language Acquisition principles to enhance personalised Chinese language learning for non-native speakers, focusing on Kyrgyzstan within the Belt and Road Initiative framework. The research employed a mixed-methods approach, combining comprehensive evaluations of three platforms (ChatGPT, DeepSeek, and Doubao), semi-structured interviews with educational experts, and theoretical analysis grounded in established theories including Krashen’s Input Hypothesis and Long’s Interaction Hypothesis. Platform assessments demonstrated varying capabilities across critical dimensions, with DeepSeek showing superior language processing performance. Expert interviews identified three primary mechanisms for personalised learning: adaptive content generation, individualised progress tracking, and immediate feedback delivery. However, cultural adaptability emerges as a significant challenge, revealing limitations in addressing sociocultural learning contexts that require human pedagogical expertise. The study developed a strategic four-phase implementation framework addressing key barriers including technical infrastructure constraints, teacher preparedness gaps, and economic limitations. Cultural bridging strategies were proposed to connect Chinese language content with local contexts, providing replicable models for diverse Belt and Road Initiative regions. The research demonstrated Artificial Intelligence Generated Content’s potential to augment rather than replace human instruction through intelligent content creation and adaptive learning pathways. While acknowledging methodological limitations including sample size constraints and absence of direct learner outcome measurements, the findings established a foundation for sustainable, culturally responsive technology integration in international Chinese language education