A human-machine collaboration framework for understanding the complementary roles of ai and teacher feedback in Chinese writing instruction

Abstract

In the teaching of Chinese writing, AI-powered automated evaluation has become a commonplace tool. It promptly identifies grammatical and lexical errors in student compositions. However, AI-generated feedback tends to be mechanistic and often fails to adequately address learners’ needs in areas such as writing logic, cultural expression, and creative thinking. How to effectively integrate these two approaches and achieve complementarity has thus emerged as a significant pedagogical issue that warrants in-depth exploration. This study investigates the complementary mechanisms and integration strategies between AI evaluation and teacher feedback within authentic teaching contexts. Through in-depth interviews with 150 learners, the research analyzes the rationale behind students’ preferences when they choose between AI-generated and instructor provided feedback. The findings indicate that AI excels in the efficient correction of linguistic form, whereas teachers play an irreplaceable role in the development of logical coherence and cultural expression. The study argues that while AI can be leveraged to improve efficiency, greater emphasis should be placed on the guidance of students to strengthen their logical thinking, which ensures that technology genuinely serves pedagogical objectives. Empirical evidence shows that such human–machine collaboration not only enhances students’ awareness of linguistic norms but also promotes their cognitive development and writing confidence. Nevertheless, technology remains an auxiliary instrument. The fundamental improvement of teaching quality ultimately depends on teachers’ deep understanding of their students and their sustained investment in the educational process.