并非所有学生都以相同方式参与：多机构在生成式人工智能辅导工具使用中的模式
Not All Students Engage Alike: Multi-Institution Patterns in GenAI Tutor Use
AI总结：
研究探讨了多机构学生在生成式人工智能辅导工具中的参与模式，发现参与类型随时间变化，高选择性机构学生更倾向于深度参与。
摘要：
生成式人工智能（GenAI）的出现为在课堂中提供个性化学习支持创造了前所未有的机会，作为大规模自动辅导系统的应用。然而，人们担忧学生可能以不支持学习的方式使用这些工具。此外，学生与GenAI辅导工具的参与可能因教学情境而异，可能导致不平等的学习体验。在本研究中，我们利用现有GenAI辅导工具和其嵌入的学习管理系统中的去标识学生交互日志。我们系统地检查了在200个班级中11,406名学生在10所高等教育机构中的参与情况，通过一个两阶段流程：首先，在对话会话层面，我们识别出四种不同的参与类型。特别是，10.4%的会话表现出
英文摘要：
The emergence of generative artificial intelligence (GenAI) has created unprecedented opportunities to provide individualized learning support in classrooms as automated tutoring systems at scale. However, concerns have been raised that students may engage with these tools in ways that do not support learning. Moreover, student engagement with GenAI Tutors may vary across instructional contexts, potentially leading to unequal learning experiences. In this study, we utilize de-identified student interaction logs from an existing GenAI Tutor and the learning management system in which it is embedded. We systematically examined student engagement (N = 11,406) with the tool across 200 classes in ten post-secondary institutions through a two-stage pipeline: First, we identified four distinct engagement types at the conversation session level. In particular, 10.4% of them were "shallow engagement" where copy-pasting behavior was prevalent. Then, at the student level, we show that students transitioned across engagement types over time. However, students who exhibited shallow engagement with the tool were more likely to remain in this mode, whereas those who engaged deeply with the tool transitioned more flexibly across engagement types. Finally, at both the session and student levels, we show substantial heterogeneity in student engagement across institution selectivity and course disciplines. In particular, students from highly selective institutions were more likely to exhibit deep engagement. Together, our study advances the understanding of how GenAI Tutors are used in authentic educational settings and provides a framework for analyzing student engagement with GenAI Tutors, with implications for responsible implementation at scale.
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