A Large-Scale Real-World Evaluation of LLM-Based Virtual Teaching Assistant

• URL: http://arxiv.org/abs/2506.17363v1
• Date: Fri, 20 Jun 2025 10:59:57 GMT
• Title: A Large-Scale Real-World Evaluation of LLM-Based Virtual Teaching Assistant
• Authors: Sunjun Kweon, Sooyohn Nam, Hyunseung Lim, Hwajung Hong, Edward Choi,
• Abstract summary: Virtual Teaching Assistants (VTAs) have the potential to enhance student learning by providing instant feedback and facilitating multi-turn interactions.<n>This study develops an LLM-based VTA and deploys it in an AI programming course with 477 graduate students.<n>We assess the feasibility of deploying VTAs in real-world classrooms and identify key challenges for broader adoption.
• Score: 19.026750427901423
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Virtual Teaching Assistants (VTAs) powered by Large Language Models (LLMs) have the potential to enhance student learning by providing instant feedback and facilitating multi-turn interactions. However, empirical studies on their effectiveness and acceptance in real-world classrooms are limited, leaving their practical impact uncertain. In this study, we develop an LLM-based VTA and deploy it in an introductory AI programming course with 477 graduate students. To assess how student perceptions of the VTA's performance evolve over time, we conduct three rounds of comprehensive surveys at different stages of the course. Additionally, we analyze 3,869 student--VTA interaction pairs to identify common question types and engagement patterns. We then compare these interactions with traditional student--human instructor interactions to evaluate the VTA's role in the learning process. Through a large-scale empirical study and interaction analysis, we assess the feasibility of deploying VTAs in real-world classrooms and identify key challenges for broader adoption. Finally, we release the source code of our VTA system, fostering future advancements in AI-driven education: \texttt{https://github.com/sean0042/VTA}.

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