A deployed LLM course assistant served 589 students across three CS courses; logs show heavy evening use and homework questions, while only about 11% of responses included AI follow-ups that students mostly ignored.
How Do Students Interact with an LLM-powered Virtual Teaching Assistant in Different Educational Settings?
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
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.
citation-role summary
background 1
citation-polarity summary
fields
cs.CY 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Investigating Student Interaction Patterns with Large Language Model-Powered Course Assistants in Computer Science Courses
A deployed LLM course assistant served 589 students across three CS courses; logs show heavy evening use and homework questions, while only about 11% of responses included AI follow-ups that students mostly ignored.