# How Do Students Interact with an LLM-powered Virtual Teaching Assistant in Different Educational Settings?

arXiv: https://arxiv.org/abs/2407.17429 · HTML: https://mlsanity.com/paper/2407.17429/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 15 Jul 2024.

## Builds on (most cited first, 3 of 3)

- [Students' Voices on Generative AI: Perceptions, Benefits, and Challenges in Higher Education](https://mlsanity.com/paper/2305.00290/) (2023)
- [Jill Watson: A Virtual Teaching Assistant powered by ChatGPT](https://mlsanity.com/paper/2405.11070/) (2024)
- [AI-Powered Learning: Making Education Accessible, Affordable, and Achievable](https://mlsanity.com/paper/2006.01908/) (2020)

## Built on by (most cited first, 4 of 4)

- [Investigating Student Interaction Patterns with Large Language Model-Powered Course Assistants in Computer Science Courses](https://mlsanity.com/paper/2509.08862/) (2025)
- [LLM-as-a-Grader: Practical Insights from Large Language Model for Short-Answer and Report Evaluation](https://mlsanity.com/paper/2511.10819/) (2025)
- ["How Do I ...?": Procedural Questions Predominate Student-LLM Chatbot Conversations](https://mlsanity.com/paper/2602.18372/) (2026)
- [A Prompt-Engineering Approach to Develop Scalable, Flexible, and Real-Time Hybrid Micro-Level Personalization in a General Purpose AI Teaching Assistant](https://mlsanity.com/paper/2609.03402/) (2026)

## Abstract (arXiv)

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.