Summary of Integrating Cognitive Ai with Generative Models For Enhanced Question Answering in Skill-based Learning, by Rochan H. Madhusudhana et al.
Integrating Cognitive AI with Generative Models for Enhanced Question Answering in Skill-based Learning
by Rochan H. Madhusudhana, Rahul K. Dass, Jeanette Luu, Ashok K. Goel
First submitted to arxiv on: 28 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the challenge of providing quick and accurate feedback in online learning by proposing a novel approach that merges Cognitive AI and Generative AI. The authors aim to create a system that not only retrieves answers but also provides explanations and helps with problem-solving. To achieve this, they employ a structured knowledge representation model called TMK (Task-Method-Knowledge) to encode skills taught in an online Knowledge-based AI course. Leveraging techniques such as Large Language Models, Chain-of-Thought, and Iterative Refinement, the authors outline a framework for generating reasoned explanations in response to learners’ questions about skills. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem in online learning: giving feedback quickly and accurately. Right now, videos can’t understand what’s being taught, so they can’t give good feedback either. Generative AI methods are better at searching for answers, but they don’t really understand what they’re saying. This makes it hard to explain skills or help with problems. The authors of this paper came up with a new way to combine Cognitive AI and Generative AI to fix these issues. |
Keywords
» Artificial intelligence » Online learning
