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 has been made toward a complete AI-powered tutoring system. In this work, we explore the development of a full-fledged intelligent tutoring system powered by state-of-the-art large language models (LLMs), covering automatic course planning and adjusting, tailored instruction, and flexible quiz evaluation. To make the system robust to prolonged interaction and cater to individualized education, the system 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. Tools are LLMs prompted to execute one specific task at a time, while memories are data storage that gets updated during education process. Statistical results and feedback from human users testify the advantage of the proposed system in long-term interaction, showcasing the benefits from structured memory control and stable reflection and reaction.

Paper Type: long

Research Area: Dialogue and Interactive Systems

Contribution Types: NLP engineering experiment, Publicly available software and/or pre-trained models

Languages Studied: English

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