限制使用AI写作能更好保持学生独立感与创作力
Effects of Varying LLM Access on Essay Writing Behavior
- 分三组测试不同AI辅助程度，观察写作行为变化
- 有限使用组作者认同感更高，修改更主动，文章更像人写
- 适合教育者思考如何合理引入AI辅助写作
本研究通过一项试点实验，考察了不同水平的大语言模型（LLM）辅助对大学生写作表现、参与度和作者认同感的影响。24名大学生被随机分配至无访问、有限访问（≤3次提问，回复不超过100词）或无限访问三组。总体作文质量在各组间无显著差异。但写作行为和作者认同感差异明显：有限访问组有62.5%的学生表示愿意以独立作品提交，远高于无限组的25%；该组在结构组织上提升更显著，且更善于制定策略性修改提示。无限访问组耗时更长，文章风格更接近AI输出，且自我表达感下降。结果表明，适度约束而非完全禁止LLM使用，有助于维持学生作者自信，同时保留AI辅助的支撑作用。
原文摘要 · Abstract (English)
Investigating the degree to which large language models (LLMs) affect teaching and learning in universities can help identify strategies for integrating LLMs in a way that supports, rather than undermines, student learning outcomes. This study examined how varying levels of LLM assistance affect writing performance, engagement, and perceived authorship. We report a pilot study in which 24 college students were randomly assigned to write a short essay with no LLM access, limited access (<=3 prompts, responses capped at 100 words), or unlimited access. Overall essay quality was statistically indistinguishable across groups. Yet writing behavior and perceived authorship diverged sharply: students with limited access reported higher ownership (62.5% would submit the essay as independent work, vs. 25% in the unlimited group), stronger organizational gains, and more strategic, revision-focused prompting. The unlimited group spent more time writing, produced essays more similar to LLM output, and reported reduced creative expression. Our findings suggest that constraining, rather than banning, LLM access may preserve authorship confidence while retaining the scaffolding benefits of AI assistance.
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