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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2504.07463 |
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| _version_ | 1866909573695143936 |
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| author | Dass, Rahul K. Madhusudhana, Rochan H. Deye, Erin C. Verma, Shashank Bydlon, Timothy A. Brazil, Grace Goel, Ashok K. |
| author_facet | Dass, Rahul K. Madhusudhana, Rochan H. Deye, Erin C. Verma, Shashank Bydlon, Timothy A. Brazil, Grace Goel, Ashok K. |
| contents | Supporting learners' understanding of taught skills in online settings is a longstanding challenge. While exercises and chat-based agents can evaluate understanding in limited contexts, this challenge is magnified when learners seek explanations that delve into procedural knowledge (how things are done) and reasoning (why things happen). We hypothesize that an intelligent agent's ability to understand and explain learners' questions about skills can be significantly enhanced using the TMK (Task-Method-Knowledge) model, a Knowledge-based AI framework. We introduce Ivy, an intelligent agent that leverages an LLM and iterative refinement techniques to generate explanations that embody teleological, causal, and compositional principles. Our initial evaluation demonstrates that this approach goes beyond the typical shallow responses produced by an agent with access to unstructured text, thereby substantially improving the depth and relevance of feedback. This can potentially ensure learners develop a comprehensive understanding of skills crucial for effective problem-solving in online environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_07463 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhanced Question-Answering for Skill-based learning using Knowledge-based AI and Generative AI Dass, Rahul K. Madhusudhana, Rochan H. Deye, Erin C. Verma, Shashank Bydlon, Timothy A. Brazil, Grace Goel, Ashok K. Artificial Intelligence Supporting learners' understanding of taught skills in online settings is a longstanding challenge. While exercises and chat-based agents can evaluate understanding in limited contexts, this challenge is magnified when learners seek explanations that delve into procedural knowledge (how things are done) and reasoning (why things happen). We hypothesize that an intelligent agent's ability to understand and explain learners' questions about skills can be significantly enhanced using the TMK (Task-Method-Knowledge) model, a Knowledge-based AI framework. We introduce Ivy, an intelligent agent that leverages an LLM and iterative refinement techniques to generate explanations that embody teleological, causal, and compositional principles. Our initial evaluation demonstrates that this approach goes beyond the typical shallow responses produced by an agent with access to unstructured text, thereby substantially improving the depth and relevance of feedback. This can potentially ensure learners develop a comprehensive understanding of skills crucial for effective problem-solving in online environments. |
| title | Enhanced Question-Answering for Skill-based learning using Knowledge-based AI and Generative AI |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2504.07463 |