Enhanced Question-Answering for Skill-based learning using Knowledge-based AI and Generative AI
This addresses the problem of enhancing learners' understanding of procedural knowledge and reasoning in online education, though it appears incremental as it builds on existing knowledge-based AI and generative AI methods.
The paper tackled the challenge of providing deep explanations for skill-based learning in online settings by using the TMK model and an LLM-based agent called Ivy, which improved the depth and relevance of feedback compared to agents using unstructured text.
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.