Work

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

Year: 2024

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2407.19393

Authors: Madhusudhana, Rochan H., Dass, Rahul K., Luu, Jeanette, Goel, Ashok K.

Institution: Georgia Institute of Technology

Language: English

Topic: Intelligent Tutoring Systems and Adaptive Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: green

Funders: National Science Foundation, Institute for Adult Learning