  In online learning, the ability to provide quick and accurate feedback to
learners is crucial. In skill-based learning, learners need to understand the
underlying concepts and mechanisms of a skill to be able to apply it
effectively. While videos are a common tool in online learning, they cannot
comprehend or assess the skills being taught. Additionally, while Generative AI
methods are effective in searching and retrieving answers from a text corpus,
it remains unclear whether these methods exhibit any true understanding. This
limits their ability to provide explanations of skills or help with
problem-solving. This paper proposes a novel approach that merges Cognitive AI
and Generative AI to address these challenges. We employ a structured knowledge
representation, the TMK (Task-Method-Knowledge) model, to encode skills taught
in an online Knowledge-based AI course. Leveraging techniques such as Large
Language Models, Chain-of-Thought, and Iterative Refinement, we outline a
framework for generating reasoned explanations in response to learners'
questions about skills.
