Practical Reflections and Application Value of AI Adaptive Learning in the Acquisition of Chinese as a Second Language

Yurui  Liu

doi:10.54691/4ehvp607

Authors

Yurui Liu

DOI:

https://doi.org/10.54691/4ehvp607

Keywords:

AI adaptive learning; second language acquisition (SLA); Chinese-teaching; Individualized learning; Teaching practice.

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.

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References

[1] Li, Y., & Zhang, L. J. (2021). Adaptive computer-assisted Chinese as second-language learning: A systematic review. ReCALL, 33(3), 287–306.

[2] Hsieh, Y.-C., & Hsu, C.-K. (2018). Personalized adaptive learning for non-native Chinese character acquisition: An experimental study. Computer Assisted Language Learning, 31(5), 521–543.

[3] Lin, H., & Su, H.-Y. (2020). Exploring adaptive computer-assisted feedback for L2 Mandarin tone learning. Language Learning & Technology, 24(3), 126–146.

[4] An, X., Chai, C. S., Li, Y., et al. (2023). Modeling students’ perceptions of artificial intelligence-assisted language learning. Computer Assisted Language Learning, 36(8), 1123–1144.

[5] Zhang, L. J., & Li, Y. (2022). Technology-enhanced personalized learning for Chinese as a second language: Current trends and future directions. Journal of Multilingual and Multicultural Development, 43(9), 781–796.

[6] Chen, H.-I., & Huang, S. H. (2022). Effects of adaptive vocabulary practice on L2 Chinese vocabulary retention. System, 107, 102815.

[7] Lee, S.-M., & Park, H. (2023). Dynamic knowledge tracing in computer-assisted second-language learning: Opportunities and challenges. Computers and Education: Artificial Intelligence, 4, 100112.

[8] Yang, C., & Wang, Y. (2023). AI-mediated automated writing feedback for L2 Chinese learners: Learner experiences and limitations. Journal of Second Language Writing, 61, 101027.

[9] Lai, C., & Li, X. (2021). Self-regulated learning in adaptive computer-assisted Chinese learning environments. Journal of Educational Technology & Society, 24(2), 112–126.

[10] Sun, P., & Lin, L. (2024). Teacher roles in AI-adaptive Chinese-as-second-language classrooms. International Journal of Educational Technology in Higher Education, 21(1).

[11] Wong, L. H., & Looi, C. K. (2019). Design principles for adaptive mobile-assisted Chinese language learning. British Journal of Educational Technology, 50(3), 1142–1158.

[12] Chai, C. S., Lin, P., & Gao, F. (2024). Artificial intelligence-enabled language learning: Motivational factors among L2 Chinese learners. Frontiers in Psychology, 15, 1340211.

[13] Wang, L., & Miller, R. T. (2022). Equity considerations for adaptive CALL for less-proficient L2 Chinese learners. ReCALL, 34(2), 184–202.

[14] Liu, Y., & Chen, W. (2025). Large-language-model-driven adaptive tutoring for Chinese second-language learners: A mixed-methods evaluation. Education Sciences, 15(4), 442.

[15] Jin, L., & De La Fuente, M. (2020). Individual differences and adaptive computer-assisted Chinese learning. Computer Assisted Language Learning, 33(7), 745–768.