Innovation in Chinese Writing Teaching Empowered by Digital Intelligence Operational Path, Classroom Practice, and Effectiveness Testing of Human Computer Co Writing Mode

Abstract

To address major problems in Chinese writing instruction, including insufficient teacher guidance throughout the writing process, delayed personalized feedback, and weak externalization of students’ writing thinking, this study constructs a human-computer collaborative writing teaching model empowered by digital intelligence. A three-level operational path is developed, consisting of collaborative brainstorming, segmented interactive writing, and multidimensional integrated evaluation and revision. Based on an intelligent writing platform, a 16-week classroom practice is conducted, and teaching effectiveness is examined through a quasi-experimental design from three dimensions: writing performance, thinking development, and learning engagement. The experimental results show that the post-test score of the experimental class is significantly higher than that of the control class, reaching 85.45 ± 5.15 compared with 78.18 ± 6.49 (p < 0.001). The adoption rate of AI suggestions reaches 53.12%, and the task completion rate reaches 92.96%. The findings confirm that the model can externalize the writing process, make writing thinking visible and iterative, and provide an operable solution for personalized guidance in large-scale writing teaching.