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.

翻译：探究大语言模型对大学教学与学习的影响程度，有助于识别既能支持学生学业成果，又不会削弱其学习效果的集成策略。本研究考察了不同层级的大语言模型辅助如何影响写作表现、参与度及作者归属感。我们报告了一项试点研究：24名大学生被随机分配撰写一篇短文，其访问权限分为无访问权限、有限访问权限（最多3次提示，每次回答限100词）和无限访问权限三组。各组间论文整体质量在统计上无显著差异，但写作行为与感知的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意识（62.5%表示会将其作为独立作业提交，而无限访问组为25%），更强的组织能力提升，以及更具战略性和侧重修订的提示行为。无限访问组花费更多时间写作，生成论文与大语言模型输出相似度更高，并报告了创意表达减弱。我们的研究表明，限制而非禁止大语言模型访问，可能有助于在保留AI辅助的脚手架效应的同时，维持学生的作者信心。