# Enhanced Question-Answering for Skill-based learning using Knowledge-based AI and Generative AI Source: [Enhanced Question-Answering for Skill-based learning using Knowledge-based AI and Generative AI]() Collection index: [llms.txt]() Data updated: 2025-10-26T04:29:27.078Z Authors: Rahul K. Dass, Rochan H. Madhusudhana, Erin C. Deye, Shashank Verma, Timothy A. Bydlon, Grace Brazil, Ashok K. Goel Published: 2025-04-10T05:25:52.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()