Research on Strategies of AI-Empowered "PLANE" Writing Instruction in Senior High School English
DOI: https://doi.org/10.62517/jhet.202615429
Author(s)
Lili Zhang
Affiliation(s)
The Middle School Attached to Ningxia University, Yinchuan, Ningxia, China
Abstract
Writing is the most heavily corrected and least effectively revised skill in senior high school English, because a teacher facing a hundred essays per task can point out errors but rarely has time to teach the rewriting. This paper develops a five-stage writing instruction framework, PLANE, in which artificial intelligence is embedded in a defined division of labour: Prompting, Learning, Assessing, Negotiating, and Editing. The framework is grounded in the cognitive process theory of writing and in recent empirical research on AI-generated feedback, which reports dependable gains in language accuracy alongside weak and variable gains in argumentation and discourse-level competence. Under the framework, the teacher designs prompts and rubrics before the task, students gather input from AI-curated model essays and corpora, an AI engine delivers first-round feedback on language form, the teacher and peers negotiate content and logic that the machine cannot judge, and students complete independent revision with version comparison. The framework was applied over twelve weeks in a Grade 11 class of 53 students in Yinchuan, Ningxia. Mean essay scores on the school rubric rose from 13.6 to 17.1 out of 25, grammar error density fell by roughly one third, and students recorded reasons for accepting or rejecting 58 percent of AI suggestions. Overreliance prevention, teacher workload, and boundaries of machine judgement are discussed.
Keywords
Artificial Intelligence; Writing Instruction; PLANE Model; Senior High School English; Feedback
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