Integrating Cognitive AI with Generative Models for Enhanced Question Answering in Skill-based Learning
This addresses the need for better educational tools in online learning, though it appears incremental as it combines existing methods like LLMs and structured knowledge models.
The paper tackles the problem of providing accurate feedback and explanations in skill-based online learning by merging Cognitive AI and Generative AI, resulting in a framework that generates reasoned explanations for learners' questions about skills.
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.