  Jill Watson, a virtual teaching assistant powered by LLMs, answers student
questions and engages them in extended conversations on courseware provided by
the instructors. In this paper, we analyze student interactions with Jill
across multiple courses and colleges, focusing on the types and complexity of
student questions based on Bloom's Revised Taxonomy and tool usage patterns. We
find that, by supporting a wide range of cognitive demands, Jill encourages
students to engage in sophisticated, higher-order cognitive questions. However,
the frequency of usage varies significantly across deployments, and the types
of questions asked depend on course-specific contexts. These findings pave the
way for future work on AI-driven educational tools tailored to individual
learning styles and course structure, potentially enhancing both the teaching
and learning experience in classrooms.
