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.

翻译：生成式人工智能（GenAI）的出现为课堂提供了前所未有的机会，能够通过自动化辅导系统大规模提供个性化学习支持。然而，人们担忧学生可能以无益于学习的方式使用这些工具。此外，学生对GenAI辅导工具的参与度可能因教学情境而异，可能导致学习体验的不平等。本研究利用现有GenAI辅导工具及其嵌入的学习管理系统中去标识化的学生交互日志，通过两阶段分析流程系统考察了十所高等教育机构200门课程中学生（N = 11,406）对该工具的使用情况：首先，我们在对话会话层面识别出四种不同的参与类型，其中10.4%属于"浅层参与"且复制粘贴行为普遍存在。随后在个体学生层面，我们发现学生随时间推移会在不同参与类型间转换，但表现出浅层参与特征的学生更可能保持该模式，而深度参与的学生则在不同类型间转换更为灵活。最后，在会话和学生两个层面，我们揭示了不同院校选拔性和课程学科间学生参与度的显著异质性：特别是顶尖选拔性院校的学生更倾向于深度参与。本研究深化了对GenAI辅导工具在真实教育环境中使用方式的理解，并为分析学生与GenAI辅导工具的互动提供了分析框架，对大规模负责任实施具有重要启示。