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.

翻译：我们提出一个名为“基于分析性逐步策略的对话式学习”（CLASS）的设计框架，用于开发高性能智能辅导系统。CLASS框架旨在赋予智能辅导系统两种关键能力：提供类似导师的逐步引导，以及实现类似导师的自然语言对话以有效吸引学习者。为赋予智能辅导系统上述能力，CLASS框架采用了两个精心策划的合成数据集。第一个脚手架数据集包含多种元素，包括问题及其对应的子问题、提示、错误解答和定制化反馈。该数据集为智能辅导系统提供了必要的解题策略，以便在对话的每一步引导学生。第二个对话数据集包含模拟的学生-导师对话，涉及应用从第一个数据集学到的解题策略。在第二个数据集中，辅导系统遵循预定义的响应模板，这有助于在交互过程中保持智能辅导系统响应的一致性和结构。这种结构化方法促进了用户反馈的无缝整合，并提供了对智能辅导系统内部决策过程的宝贵见解，从而允许系统的持续改进和优化。我们还提出了一个概念验证的智能辅导系统，名为SPOCK，它是使用CLASS框架训练的，专注于大学水平的入门生物学内容。我们为SPOCK的初步评估制定了一个精心设计的协议，检查其响应的事实准确性和相关性等。生物学领域的专家给出了积极评价，特别强调了SPOCK将问题分解为可管理的子问题并提供逐步指导学生的能力。