Abstract Book of the 9th International Conference on Advanced Research in Education
Year: 2025
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Exploring Student Acceptance of Generative AI Tools in Providing Feedback for Non-Fiction Chinese Writing Instruction
Lu Liu
ABSTRACT:
Non-fiction writing is pivotal in the tertiary Chinese language education of Hong Kong, enhancing students’ creative expression, narrative building, argumentation, and literary style. Nevertheless, an intense curriculum and limited teaching time often restrict adequate writing practice and delay feedback, adversely affecting student performance and learning experiences. This research investigates the integration of generative artificial intelligence (AI) tools, like GPT-4, to augment traditional feedback mechanisms by offering prompt and personalized feedback on student writings. The main goal is to assess student receptivity to AI-generated feedback and its comparison with traditional teacher feedback. Participants were split into two groups: one receiving feedback from both AI and teachers, and a control group receiving only teacher feedback. By analyzing student revisions, this study assesses enhancements in content/theme, linguistic expression, and structural coherence.
Initial results suggest that students receiving combined AI and teacher feedback performed similarly to those in the control group. AI feedback was predominantly accepted in aspects concerning language usage and structure, whereas teacher feedback was more influential in content and thematic exploration. Acceptance levels for both feedback types were comparably high among students. Significantly, AI-generated recommendations were specific and focused, leading to heightened motivation and a stronger inclination to revise. This study underscores the educational benefits of employing AI tools in writing instruction, particularly in improving feedback timeliness and fostering learner independence.
Keywords: Student acceptance, AI-generated feedback, Chinese writing, non-fiction writing, higher education