Practical Reflections and Application Value of AI Adaptive Learning in the Acquisition of Chinese as a Second Language
TL;DR
This study proposes a multi-dimensional optimization strategy based on authentic classroom settings in Chinese language teaching that offers practical insights for the routine integration of intelligent teaching models into Chinese language education and contributes to the high-quality development of international Chinese language education.
Abstract
The digital transformation of education continues to advance, with artificial intelligence and adaptive technologies gradually integrating into teaching scenarios for Chinese as a foreign language, offering innovative pathways to transform traditional, rigid second-language instruction models. Currently, universities in China and socialized Chinese language education institutions generally adopt standardized, large-scale classroom instruction. Uniform curricula and fixed teaching paces struggle to meet the personalized development needs of foreign learners from diverse linguistic backgrounds and varying proficiency levels. While standardized models suit large-scale educational management systems, they overlook the individual differences inherent in second-language acquisition, thereby constraining long-term improvements in learners' overall Chinese competence and limiting further advancements in the quality of Chinese language teaching. Grounded in Krashen's second-language acquisition theory and the input hypothesis, this paper systematically analyzes the application value of AI-driven adaptive learning in Chinese second-language acquisition, drawing on practical experiences from frontline teaching. It identifies multidimensional real-world challenges in implementing intelligent teaching technologies, focusing on four key areas: technological compatibility, integration between pedagogy and technology, teacher and student digital literacy, and assessment systems. AI-adaptive learning can precisely match diverse learner profiles, optimize Chinese language competency development frameworks, and reconstruct an intelligent classroom ecosystem. However, current practices often suffer from low accuracy in adapting technology to Chinese language contexts, superficial integration of technology and pedagogy, insufficient digital literacy among teachers and students, and overly simplistic or rigid evaluation mechanisms. To effectively address these challenges and fully unlock the educational potential of intelligent technologies, this study proposes a multi-dimensional optimization strategy based on authentic classroom settings in Chinese language teaching. The strategy includes four core directions: iterative refinement of specialized Chinese language technologies, deep integration of teaching and technology, systematic development of teacher and student digital competencies, and enhancement of diversified assessment mechanisms. This research offers practical insights for the routine integration of intelligent teaching models into Chinese language education and contributes to the high-quality development of international Chinese language education.