TEACHER-CURATED AI FOR COMMUNICATIVE CHINESE ACQUISITION IN UNIVERSITIES OF KAZAKHSTAN
DOI:
https://doi.org/10.37943/LHFX3759Keywords:
Artificial Intelligence; Chinese character acquisition; Teaching Chinese as a Second Language; communicative competence; pedagogical scaffolding; semantic awareness; digital transformation; Kazakhstan higher education;Abstract
As China's global economic and political influence continues to expand, the demand for Chinese language proficiency in Kazakhstan has reached an all-time high, yet students frequently struggle with the mastery of the logographic writing system, which often remains isolated from communicative practice. This study investigates the challenge of bridging the gap between mechanical character acquisition and functional communication among first-year students at Kazakh Ablai Khan University, set against the strategic national mandate for digital transformation and the integration of artificial intelligence into the education system. To address systemic orthographic errors and a lack of semantic awareness, the research evaluated a teacher-curated artificial intelligence framework designed to shift pedagogy from rote memorization to active semantic use. The methodology employed a quasi-experimental, mixed-methods design involving 29 students during the 2025–2026 academic year, beginning with the analysis of a diagnostic corpus of over 120 homework assignments to categorize baseline errors. This was followed by an eight-week intervention where the experimental group utilized specialized artificial intelligence-assisted "Free Chat" sessions for real-time scaffolding. Results from an independent samples t-test revealed a significant performance divergence, with the experimental group increasing character accuracy from 68.50% to 84.20%, compared to a control group mean of 71.80%. Qualitative analysis of interaction logs and interviews demonstrated that the artificial intelligence successfully mitigated cognitive load by deconstructing complex characters into manageable semantic narratives, though students noted a "digital-analog gap" regarding handwriting recall. The study concludes that teacher-curated artificial intelligence serves as a powerful cognitive bridge that corrects fossilized structural errors and fosters communicative risk-taking. These findings provide a scalable blueprint for integrating human-centric digital tools in university language departments, emphasizing that curated artificial intelligence enhances rather than replaces the cognitive processes essential for autonomous linguistic competence.
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