考察学生与教学型AI助手在议论文写作中的互动及其对学生写作质量的影响
Examining Student Interactions with a Pedagogical AI-Assistant for Essay Writing and their Impact on Students Writing Quality
AI总结：
研究探讨学生与教学型AI助手在议论文写作中的互动及其对写作质量的影响，发现主动写作和反馈共享更有效，建议教师鼓励学生主动参与并优化AI系统设计。
摘要：
学生与生成式人工智能（GenAI）之间的动态互动及其与写作质量的关系仍处于探索阶段。尽管大多数研究探讨了通用型GenAI如何支持写作，但较少研究调查了学生在写作过程中不同阶段与教学型系统互动的方式。为填补这一空白，我们评估了一个由GenAI驱动的议论文写作助手（EWA），旨在帮助高等教育学生进行论证写作。基于32名本科生在两小时内写作 sessions 中的1,282条互动日志，使用序列模式挖掘和K-均值聚类来识别行为模式。两个聚类出现：聚类1强调大纲规划和论文结构，而聚类2则聚焦内容发展。曼-惠特尼U检验显示在论文组织维度上存在中等效应量（r=0.36），聚类1得分更高。定性分析表明，表现较好的学生积极写作并分享论文部分给EWA以获取反馈，而不是被动地通过提问互动。这些发现为教学和系统设计提供了启示。教师可以鼓励主动参与，而未来的EWA可能整合自动标记和监控以促使学生从提问转向写作，从而充分利用GenAI支持的学习效益。
英文摘要：
The dynamic nature of interactions between students and GenAI, as well as their relationship to writing quality, remains underexplored. While most research has examined how general-purpose GenAI can support writing, fewer studies have investigated how students interact with pedagogically designed systems across different phases of the writing process. To address this gap, we evaluated a GenAI-driven essay-writing assistant (EWA) designed to support higher education students in argumentative writing. Drawing on 1,282 interaction logs from 32 undergraduates during a two-hour writing session, Sequential Pattern Mining and K-Means clustering were used to identify behavioral patterns. Two clusters emerged: Cluster 1 emphasized outline planning and essay structure, while Cluster 2 focused on content development. A Mann-Whitney U test revealed a moderate effect size (r = 0.36) in the essay Organization dimension, with Cluster 1 showing higher scores. Qualitative analysis indicated that students with better performance actively wrote and shared essay sections with EWA for feedback, rather than interacted passively by asking questions. These findings suggest implications for teaching and system design. Teachers can encourage active engagement, while future EWAs may integrate automatic labeling and monitoring to prompt students to move from questioning to writing, enabling fuller benefits from GenAI-supported learning.
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。
