Intelligent tutoring systems leverage AI models of expert learning and student knowledge to deliver personalized tutoring to students. While these intelligent tutors have demonstrated improved student learning outcomes, it is still unclear how teachers might integrate them into curriculum and course planning to support responsive pedagogy. In this paper, we conducted a design study with five teachers who have deployed Apprentice Tutors, an intelligent tutoring platform, in their classes. We characterized their challenges around analyzing student interaction data from intelligent tutoring systems and built VisTA (Visualizations for Tutor Analytics), a visual analytics system that shows detailed provenance data across multiple coordinated views. We evaluated VisTA with the same five teachers, and found that the visualizations helped them better interpret intelligent tutor data, gain insights into student problem-solving provenance, and decide on necessary follow-up actions - such as providing students with further support or reviewing skills in the classroom. Finally, we discuss potential extensions of VisTA into sequence query and detection, as well as the potential for the visualizations to be useful for encouraging self-directed learning in students.

翻译：智能辅导系统借助专家学习与学生知识的人工智能模型，为学生提供个性化辅导。尽管这类智能导师已被证明能提升学生学习成果，但教师如何将其整合到课程与教学计划中以支持响应式教学，仍不明确。本文与五位在课堂中部署了智能辅导平台Apprentice Tutors的教师开展了一项设计研究。我们归纳了他们在分析智能辅导系统产生的学生交互数据时面临的挑战，并构建了VisTA（辅导分析可视化系统）——一个通过多联动视图展示详细溯源数据的可视分析系统。我们邀请这五位教师对VisTA进行评测，发现该可视化系统帮助他们更准确地解读智能辅导数据、洞察学生解题过程溯源、并决定必要的后续行动（如提供进一步支持或在课堂中复习相关技能）。最后，我们讨论了VisTA在序列查询与检测方面的潜在扩展，以及该可视化工具在激励学生自主学习中可能发挥的作用。