更聪明的AI助写反而让低水平学生写作表面漂亮，但难提升真实能力。
The Crutch or the Ceiling? How Different Generations of LLMs Shape EFL Student Writings
- 对比ChatGPT前后学生作文，分析AI助写对语言发展的影响
- 高级AI提升分数和词汇多样性，但专家评分反而下降
- 需区分思想引导与文字生成，避免沦为应付作业的工具
大型语言模型（LLMs）的快速发展使其成为提升学生写作的重要工具。本研究探讨了不同代际LLMs对中学阶段英语作为外语（EFL）学生写作的影响及其局限性。现有研究多关注输出质量，而本研究聚焦于LLM演进过程及其对学生写作发展的实际作用，评估更智能的模型是否真正起到学习支架作用，还是仅作为补偿性拐杖。通过分析ChatGPT发布前后的学生作文，结合专家定性评分与定量指标（可读性测试、皮尔逊相关系数、MTLD等），结果表明：先进模型显著提升了低水平学习者的评分与词汇多样性，可能掩盖其真实语言能力；但高水平辅助与人类专家评分呈负相关，显示表面流畅却缺乏深层连贯性。要实现真正的学习，教学应从关注输出转向验证学习过程，明确区分概念引导与文本生成，并在学习者最近发展区内合理配置AI功能。
原文摘要 · Abstract (English)
The rapid evolution of Large Language Models (LLMs) has made them powerful tools for enhancing student writing. This study explores the extent and limitations of LLMs in assisting secondary-level English as a Foreign Language (EFL) students with their writing tasks. While existing studies focus on output quality, our research examines the developmental shift in LLMs and their impact on EFL students, assessing whether smarter models act as true scaffolds or mere compensatory crutches. To achieve this, we analyse student compositions assisted by LLMs before and after ChatGPT's release, using both expert qualitative scoring and quantitative metrics (readability tests, Pearson's correlation coefficient, MTLD, and others). Our results indicate that advanced LLMs boost assessment scores and lexical diversity for lower-proficiency learners, potentially masking their true ability. Crucially, increased LLM assistance correlated negatively with human expert ratings, suggesting surface fluency without deep coherence. To transform AI-assisted practice into genuine learning, pedagogy must shift from focusing on output quality to verifying the learning process. Educators should align AI functions, specifically differentiating ideational scaffolding from textual production, within the learner's Zone of Proximal Development.
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