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.

翻译：学生与生成式人工智能（GenAI）之间动态交互的本质及其与写作质量的关系，目前仍未得到充分研究。尽管多数研究探讨了通用型GenAI如何辅助写作，但较少有研究关注学生在写作过程的不同阶段如何与教学型系统进行交互。为填补这一空白，我们评估了一款专为支持高等教育学生进行议论文写作而设计的GenAI驱动型论文写作助手（EWA）。基于32名本科生在两小时写作环节中产生的1,282条交互日志，我们采用序列模式挖掘和K均值聚类方法识别行为模式。分析得到两个聚类：聚类1侧重于提纲规划与文章结构，聚类2则聚焦于内容拓展。曼-惠特尼U检验显示，在文章组织维度上存在中等效应量（r=0.36），聚类1的学生在该维度得分更高。定性分析表明，表现较好的学生倾向于主动撰写论文段落并与EWA分享以获取反馈，而非仅通过提问进行被动交互。这些发现对教学与系统设计具有启示意义：教师可鼓励学生主动参与写作过程，而未来的EWA系统可整合自动标注与监测功能，引导学生从提问转向写作，从而更充分地发挥GenAI辅助学习的效益。