A Study on the Application of Generative AI Tools for Chinese Writing Feedback in Non-Fiction Writing Instruction

Authors: Stephanie Liu Lu

Abstract:

The course University Chinese, integral to the curriculum of higher education institutions in Hong Kong, significantly enhances students' creative expression, narrative construction, argumentative prowess, and literary skills through its focus on non-fiction writing. Despite its importance, the breadth of the syllabus, combined with limited classroom time, frequently restricts sufficient practice opportunities and leads to delayed feedback, which adversely affects students' preparation for assessments. This paper explores the utility of generative artificial intelligence (AI) tools in offering personalized and instantaneous feedback for writing tasks. The principal aim of this research is to assess student receptiveness to feedback generated by AI and compare it to the traditional feedback provided solely by human instructors. To this end, participants will be systematically divided into two groups: one will receive feedback from both instructors and AI tools, while a control group will receive feedback exclusively from instructors. The study will meticulously analyze the revisions made to texts after receiving feedback, with a particular focus on enhancements in the quality of content and language proficiency. This investigation seeks to ascertain whether AI tools can not only augment the efficiency of teaching practices but also foster autonomous learning among students and substantially improve the overall quality of their written work.

Keywords: AI-generated feedback, Chinese writing, non-fiction writing, student receptiveness