Japanese writing instruction in foreign language education continues to face challenges such as low correction efficiency, limited error identification, and insufficient personalized feedback. This study examines the application of a BERT-based intelligent grading system within a blended teaching framework to address these issues. The research explores three key questions: (1) how BERT can be leveraged for automatic detection of grammatical, spelling, and sentence structure errors in Japanese writing; (2) how the system can be integrated into blended teaching; and (3) what measurable impact it has on student writing outcomes. We developed a BERT-based encoder–decoder model and conducted a controlled experiment involving an experimental group ( n =150) using the system and a control group ( n =150) relying on manual grading. The results showed that the experimental group achieved higher writing accuracy (89.3 vs. 79.5), improved logical coherence (4.4 vs. 3.7 on a 5-point rubric), and faster feedback (average 4.8 minutes vs. 26 minutes). The system also achieved a grammar error detection F1-score of 84.4%, outperforming traditional RNN and Transformer models. Despite its strengths, limitations persist in addressing discourse-level coherence and context-sensitive semantics. This study offers empirical evidence for integrating deep learning with pedagogy, providing a scalable and effective approach to enhancing writing instruction in second language education.
Ping Yan (Thu,) studied this question.