VTutor for High-Impact Tutoring at Scale: Managing Engagement and Real-Time Multi-Screen Monitoring with P2P Connections

May 12, 2025 Β· Declared Dead Β· πŸ› arXiv.org

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Authors Eason Chen, Xinyi Tang, Aprille Xi, Chenyu Lin, Conrad Borchers, Shivang Gupta, Jionghao Lin, Kenneth R Koedinger arXiv ID 2505.07736 Category cs.HC: Human-Computer Interaction Citations 1 Venue arXiv.org Last Checked 4 months ago
Abstract
Hybrid tutoring, where a human tutor supports multiple students in learning with educational technology, is an increasingly common application to deliver high-impact tutoring at scale. However, past hybrid tutoring applications are limited in guiding tutor attention to students that require support. Specifically, existing conferencing tools, commonly used in hybrid tutoring, do not allow tutors to monitor multiple students' screens while directly communicating and attending to multiple students simultaneously. To address this issue, this paper introduces VTutor, a web-based platform leveraging peer-to-peer screen sharing and virtual avatars to deliver real-time, context-aware tutoring feedback at scale. By integrating a multi-student monitoring dashboard with AI-powered avatar prompts, VTutor empowers a single educator or tutor to rapidly detect off-task or struggling students and intervene proactively, thus enhancing the benefits of one-on-one interactions in classroom contexts with several students. Drawing on insight from the learning sciences and past research on animated pedagogical agents, we demonstrate how stylized avatars can potentially sustain student engagement while accommodating varying infrastructure constraints. Finally, we address open questions on refining large-scale, AI-driven tutoring solutions for improved learner outcomes, and how VTutor could help interpret real-time learner interactions to support remote tutors at scale. The VTutor platform can be accessed at https://ls2025.vtutor.ai. The system demo video is at https://ls2025.vtutor.ai/video.
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