A Web Engineering Approach to AI-Enabled Japanese Tutoring via Instructional Design Pattern Mining
DOI:
https://doi.org/10.13052/jwe1540-9589.2577Keywords:
AI empowerment, Japanese tutoring, Web application instructional design modelAbstract
Despite the proliferation of artificial intelligence (AI)-enabled web applications for Japanese language learning, existing platforms suffer from three critical challenges: insufficient integration of AI capabilities with pedagogical design, limited scenario adaptability across proficiency levels, and suboptimal lightweight deployment on the browser side. To address these gaps and explore instructional design patterns tailored to diverse Japanese learning needs, this study focuses on web application technology implementation and interaction design as the core analytical dimensions. Three typical AI-enabled Japanese tutoring web applications were selected as research objects, and 192 Japanese learners participated in the empirical research. Quantitative data were obtained through technical disassembly, questionnaire surveys, and web-based behavior tracking; learning effects were verified by comparative experiments, and the weight of instructional design elements was determined via the analytic hierarchy process, finally extracting core instructional design modes suitable for the mainstream Japanese learning stages from beginner to upper-intermediate (Japanese-Language Proficiency Test [JLPT] N5–N2), with preliminary applicability discussion for the advanced stage (N1 and above). The results show that web-based lightweight AI technology deployment and real-time AI interactive feedback design are the core elements, with a combined weight of over 40%. There is a significant positive correlation between web-based AI technology implementation and user satisfaction, with the highest implementation effect and user acceptance in listening, speaking, and grammar modules. Compared with traditional Japanese web learning platforms, AI-enabled instructional design can improve learners’ listening and speaking abilities by 58.0%(M=58.0,SD=4.2,95% CI [56.4, 59.6], Cohen’s d=1.82, large effect), grammar analysis abilities by 46.5% (M=46.5,SD=5.1,95%CI [44.8, 48.2], Cohen’s d=1.45, large effect), and enhance learning sustainability by 85.7% (M=85.7,SD=6.3,95%CI[83.5,87.9], Cohen’s d=2.01, large effect), based on N5–N2 learner data with robust statistical power. Three core instructional design modes were identified, adapted to zero-based-N4 fragmented learning, N3–N2 systematic learning, and N1+ immersive learning, respectively. This study clarifies the core elements and optimization direction of such applications, providing standardized and practical theoretical references and paths for their design, development, and teaching practice.
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