Empowering Private Tutoring by Chaining Large Language Models
Yulin Chen Ning Ding Hai-Tao Zheng Zhiyuan Liu Maosong Sun Bowen Zhou
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-interaction, reflection, and 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
from learning logs demonstrate the effectiveness and mechanism of each tool
usage. Subjective feedback from human users reveal the usability of each
function, and comparison with ablation systems further testify the benefits of
the designed processes in long-term interaction.