Volume 3,Issue 7
Effectiveness Analysis of a Personalized English Writing Feedback System Based on Advanced Language Models
Recent advancements in natural language processing have catalyzed transformative approaches to second language writing instruction. This study introduces a personalized English writing feedback system that harnesses state-of-the-art generative language models to deliver real-time, adaptive feedback tailored to individual learners. Over the course of a 12-week intervention involving 80 undergraduate students, the system was integrated into writing instruction with the aim of fostering self-directed revision and reflective writing practices. Drawing on both quantitative and qualitative data—including writing samples, student surveys, and teacher interviews—the results reveal significant improvements in writing quality, learner autonomy, and revision strategy use in the experimental group compared to traditional feedback approaches. This research contributes to the ongoing discourse on technology-enhanced language learning, underscoring the complementary role of human pedagogical insight and algorithmic feedback in optimizing instructional outcomes.
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