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

TL;DR

This paper introduces a novel framework combining Cognitive AI and Generative AI, using structured knowledge and advanced language models to improve explanation and understanding in skill-based online learning.

Contribution

It presents a new integrated approach that merges Cognitive AI with Generative AI, utilizing the TMK model and advanced LLM techniques for enhanced educational explanations.

Findings

Framework effectively generates reasoned explanations.

Combines structured knowledge with LLMs for better understanding.

Addresses limitations of traditional AI in skill explanation.

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.