Awaking the Slides: A Tuning-free and Knowledge-regulated AI Tutoring System via Language Model Coordination
This work addresses the challenge of leveraging multi-modal slide content for personalized student learning, though it appears incremental as it builds on existing language model coordination techniques.
The authors tackled the problem of converting lecture slides into interactive lectures by developing Slide2Lecture, a tuning-free AI tutoring system that structures teaching actions and generates responsive interactions, with evaluations showing it outperforms other implementations and has been deployed in over 3K lecture sessions with 200K student interactions.
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 slide. For teachers and developers, Slide2Lecture enables customization to cater to personalized demands. The evaluation rated by annotators and students shows that Slide2Lecture is effective in outperforming the remaining implementation. Slide2Lecture's online deployment has made more than 200K interaction with students in the 3K lecture sessions. We open source Slide2Lecture's implementation in https://anonymous.4open.science/r/slide2lecture-4210/.