Awaking the Slides: A Tuning-free and Knowledge-regulated AI Tutoring System via Language Model Coordination
Daniel Zhang-Li, Zheyuan Zhang, Jifan Yu, Joy Lim Jia Yin, Shangqing, Tu, Linlu Gong, Haohua Wang, Zhiyuan Liu, Huiqin Liu, Lei Hou, Juanzi Li

TL;DR
This paper introduces Slide2Lecture, a tuning-free, knowledge-regulated AI tutoring system that converts lecture slides into interactive, structured teaching sessions, enhancing personalized learning and engagement.
Contribution
The paper presents a novel system that effectively transforms slides into interactive lectures without tuning, enabling customization and improved student engagement.
Findings
Outperforms existing methods in annotation and student ratings.
Over 200,000 interactions in 3,000 lecture sessions demonstrate effectiveness.
Open-sourced implementation facilitates further research and deployment.
Abstract
The vast pre-existing slides serve as rich and important materials to carry lecture knowledge. However, effectively leveraging lecture slides to serve students is difficult due to the multi-modal nature of slide content and the heterogeneous teaching actions. We study the problem of discovering effective designs that convert a slide into an interactive lecture. We develop Slide2Lecture, a tuning-free and knowledge-regulated intelligent tutoring system that can (1) effectively convert an input lecture slide into a structured teaching agenda consisting of a set of heterogeneous teaching actions; (2) create and manage an interactive lecture that generates responsive interactions catering to student learning demands while regulating the interactions to follow teaching actions. Slide2Lecture contains a complete pipeline for learners to obtain an interactive classroom experience to learn the…
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Taxonomy
TopicsIntelligent Tutoring Systems and Adaptive Learning · Topic Modeling
MethodsSparse Evolutionary Training
