Integrating Cognitive AI with Generative Models for Enhanced Question Answering in Skill-based Learning

• URL: http://arxiv.org/abs/2407.19393v2
• Date: Fri, 2 Aug 2024 21:06:51 GMT
• Title: Integrating Cognitive AI with Generative Models for Enhanced Question Answering in Skill-based Learning
• Authors: Rochan H. Madhusudhana, Rahul K. Dass, Jeanette Luu, Ashok K. Goel,
• Abstract summary: 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.
• Score: 3.187381965457262
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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