不同LLM访问权限对论文写作行为的影响
Effects of Varying LLM Access on Essay Writing Behavior
AI总结：
通过控制LLM访问权限（无访问、有限访问、无限访问）的随机实验，发现有限访问能保持学生作者信心和写作策略，而无限访问降低创意表达。
摘要：
调查大型语言模型（LLM）对大学教学和学习的影响程度，有助于确定整合LLM的策略，以支持而非削弱学生的学习成果。本研究考察了不同水平的LLM辅助如何影响写作表现、参与度和感知作者身份。我们报告了一项初步研究，其中24名大学生被随机分配，在无LLM访问、有限访问（最多3次提示，每次回复限制100字）或无限访问条件下撰写一篇短文。各组之间的整体论文质量在统计上无显著差异。然而，写作行为和感知作者身份出现显著分化：有限访问组的学生报告了更高的所有权（62.5%愿意将论文作为独立作品提交，而无限访问组为25%）、更强的组织收益以及更具策略性和以修改为中心的提示。无限访问组花费更多时间写作，产生的论文与LLM输出更相似，并报告了创意表达减少。我们的研究结果表明，限制而非禁止LLM访问，可以在保留AI辅助的支架优势的同时，保持作者的信心。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
