Exploring Human-AI Collaborative Process Assessment in English Writing in College Education
To address the declining validity of English writing assessment in higher vocational education caused by generative AI and the difficulty of reflecting students’ true language and critical thinking abilities through final drafts alone, this study localizes and adapts the AISAR process evidence collection framework to construct a process evidence collection mechanism comprising AI prompt chains, content selection and rejection rationales, revision marks, and reflection reports. It establishes a human-AI collaborative assessment workflow of “AI standardized preliminary evaluation + teacher higher-order judgment.” Through classroom action research, it is found that human-AI collaborative assessment based on process evidence can guide students’ writing revisions from surface-level language correction toward deeper thinking optimization, help teachers capture the cognitive trajectories of student-AI interaction, and achieve an expansion from single final-draft evaluation to multidimensional assessment of “textual output + cognitive process.” The study also identifies practical dilemmas encountered in implementation and proposes targeted optimization pathways, with the aim of providing practical references for intelligent assessment reform in higher vocational English education.
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