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.

翻译：人工智能已应用于在线教育的诸多方面以促进教学。然而，目前鲜有研究致力于构建完整的AI驱动型辅导系统。本文探索开发一套由最先进大语言模型（LLMs）驱动的全功能智能辅导系统，涵盖自动课程规划与调整、个性化教学以及灵活的测验评估。为增强系统在长期互动中的鲁棒性并适配个性化教育，系统被分解为三个相互关联的核心过程：交互、反思与反应。每个过程均通过链式调用基于LLM的工具以及动态更新的记忆模块实现。工具是经提示词设计、每次执行特定任务的LLM，而记忆则是伴随教学过程持续更新的数据存储。学习日志的统计结果表明了各工具使用的有效性及其运行机制，人类用户的主观反馈揭示了各功能的可用性。与消融系统的对比进一步验证了所设计过程在长期互动中的优势。