  We present a design framework called Conversational Learning with Analytical
Step-by-Step Strategies (CLASS) for building advanced Intelligent Tutoring
Systems (ITS) powered by high-performance Large Language Models (LLMs). The
CLASS framework empowers ITS with two key capabilities. First, through a
carefully curated scaffolding dataset, CLASS equips ITS with essential
problem-solving strategies, enabling it to provide tutor-like, step-by-step
guidance to students. Second, by using a dynamic conversational dataset, CLASS
assists ITS in facilitating natural language interactions, fostering engaging
student-tutor conversations. The CLASS framework also provides valuable
insights into ITS' internal decision-making process which allows seamless
integration of user feedback, thus enabling continuous refinement and
improvement. We also present a proof-of-concept ITS, referred to as SPOCK,
which is trained using the CLASS framework with a focus on introductory
college-level 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 encouraging
responses to students. Code and models are available at
https://github.com/luffycodes/Tutorbot-Spock.
