Who is mimicking whom? The mutual shaping effect between AI and college students’ english writing

Who is mimicking whom when university EFL writers and ChatGPT interact over a sustained period? This 10-week longitudinal study tracked 20 first-year Chinese undergraduates across a Pre-AI baseline, an eight-week ChatGPT-assisted intervention, and a Post-AI independent phase, drawing on corpus analysis of 400 essays alongside 320 interaction logs and triangulated qualitative data. Twenty of twenty-two linguistic features showed large phase effects, but the more telling pattern was selective: features carrying semantic or pragmatic weight, including nominalisation density, hedging, and rebuttal quality, were retained well after AI withdrawal, whereas stylistically marked but optional structures such as cleft constructions and formal connectives reverted towards baseline. Students appear to have consolidated the resources that allowed them to mean differently rather than only to sound differently. Two further findings complicate received accounts: an AI-generated content (AIGC) paradox in which detection rates registered measurable signal even in pre-intervention writing and tracked closely with holistic ratings (r = .862), a pattern more readily read as evidence of formal academic register than of machine authorship; and a recursive coupling in which student prompts and AI responses progressively shape each other. The study proposes a Mutual Adaptation Framework that takes the learner-tool system, rather than the learner alone, as the developmental unit.