Empowering Private Tutoring by Chaining Large Language Models

Anonymous

Abstract: Artificial intelligence has been applied in various aspects of online education to facilitate teaching and learning. However, few approaches have been made towards a complete AI-powered tutoring system.In this work, we explore the development of a full-fledged intelligent tutoring system based on large language models (LLMs). The proposed system \modelname, powered by state-of-the-art LLMs, is equipped with automatic course planning and adjusting, informative instruction, and adaptive quiz offering and evaluation.\modelname\ is decomposed into three inter-connected core processes-\textit{interaction}, \textit{reflection}, and \textit{reaction}. Each process is implemented by chaining LLM-powered tools along with dynamically updated memory modules. To demonstrate the mechanism of each working module and the benefits of structured memory control and adaptive reflection, we conduct a wide range of analysis based on statistical results and user study. The analysis shows the designed processes boost system consistency and stability under long-term interaction and intentional disruptions, with up to 5\% and 20\% increase in performance respectively. Meanwhile, we also compare the system with scripts from real-world online learning platform and discuss the potential issues unique to LLM-based systems.

Paper Type: long

Research Area: Dialogue and Interactive Systems

Languages Studied: English