The Practical Research of Generative AI Assisted Japanese L2 Teaching Centered on Telling Chinese Stories

Yiching Tsai

doi:10.70088/39thh658

Authors

Yiching Tsai Department of Foreign Languages, Zhaoqing University, Zhaoqing, China Author

DOI:

https://doi.org/10.70088/39thh658

Keywords:

generative AI, japanese language teaching, cross-cultural communication, quasi-experimental study, curriculum design

Abstract

Digital foreign language education and curriculum design require innovative pedagogical approaches to cultivate students' cross-cultural communication competence. This quasi-experimental study explores a hierarchical generative artificial intelligence assignment system that integrates the narrative of Chinese cultural heritage into zero-beginner Japanese language courses for business English majors, aiming to evaluate its effectiveness in enhancing language proficiency and cultural awareness. Two parallel classes comprising thirty-two experimental and thirty-one control students were selected for a sixteen-week instructional period. The experimental group engaged in nine categories of AI-supported writing and recording tasks supplemented by classroom presentations, whereas the control group completed traditional grammar exercises without AI assistance. Data were collected through pre- and post-intervention five-point Likert scale questionnaires, multi-dimensional process scores, final Japanese compositions, and student presentation manuscripts. Independent and paired t-tests along with correlation analysis were employed for quantitative measurement, while hierarchical text coding was applied for qualitative analysis. Results indicated that all post-test dimensions of the experimental group were significantly higher than those of the control group (p<0.001, Cohen's d>0.8). The average AI assignment score reached 91.59, the highest among all assessed indicators. Approximately 80.77% of students utilized AI tools for composition writing, and 88.47% achieved a medium level or above in final Chinese-themed writing. Qualitative analysis revealed notable quality disparities among high, medium, and low-performing student texts, alongside potential risks of excessive AI reliance and cultural mistranslation. The proposed AI assignment system significantly enhances students' Japanese cultural writing and oral expression abilities while strengthening cultural self-confidence. A five-step closed-loop teaching framework is proposed to balance AI advantages with potential drawbacks, providing replicable pedagogical practices for non-Japanese majors.

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