We present a design framework called Conversational Learning with Analytical Step-by-Step Strategies (CLASS) for developing high-performance Intelligent Tutoring Systems (ITS). The CLASS framework aims to empower ITS with with two critical capabilities: imparting tutor-like step-by-step guidance and enabling tutor-like conversations in natural language to effectively engage learners. To empower ITS with the aforementioned capabilities, the CLASS framework employs two carefully curated synthetic datasets. The first scaffolding dataset encompasses a variety of elements, including problems, their corresponding subproblems, hints, incorrect solutions, and tailored feedback. This dataset provides ITS with essential problem-solving strategies necessary for guiding students through each step of the conversation. The second conversational dataset contains simulated student-tutor conversations that involve the application of problem-solving strategies learned from the first dataset. In the second dataset, the tutoring system adheres to a pre-defined response template, which helps to maintain consistency and structure in ITS's responses during its interactions. This structured methodology facilitates seamless integration of user feedback and yields valuable insights into ITS's internal decision-making process, allowing for continuous refinement and improvement of the system. We also present a proof-of-concept ITS, referred to as SPOCK, trained using the CLASS framework with a focus on college level introductory biology content. A carefully constructed protocol was developed for SPOCK's preliminary evaluation, examining aspects such as the factual accuracy and relevance of its responses. Experts in the field of biology offered favorable remarks, particularly highlighting SPOCK's capability to break down questions into manageable subproblems and provide step-by-step guidance to students.

本文介绍了一种名为Conversational Learning with Analytical Step-by-Step Strategies (CLASS)的设计框架，为开发高性能的智能辅导系统(Intelligent Tutoring Systems)提供了支持，该框架旨在为ITS赋予像导师一样的逐步指导和使用自然语言进行对话的能力以有效吸引学习者。本设计框架采用了两个精心策划的合成数据集，一个是满足学生进行问题解决的策略的脚手架数据集，另一个是包含从先前数据集中学到的解决问题策略的模拟学生-导师对话数据集，两者相结合构成了具有不断完善提高能力的智能辅导系统。

基于学习科学原理的教育辅导聊天机器人 CLASS Meet SPOCK