用知识模型提升AI对技能问题的深度解释能力
Enhanced Question-Answering for Skill-based learning using Knowledge-based AI and Generative AI
- 基于TMK模型与大语言模型，迭代生成解释
- 生成内容涵盖目的、因果和组合逻辑，更深入
- 适合需要深层理解技能的学习场景
在线学习中支持学习者理解所授技能是长期挑战。尽管练习题和聊天代理可在有限情境下评估理解程度，但当学习者寻求关于操作知识（如何做）和推理原因（为何发生）的解释时，这一挑战尤为突出。我们假设，通过任务-方法-知识（TMK）模型这一基于知识的人工智能框架，可显著增强智能代理理解并解释学习者技能相关问题的能力。本文提出Ivy，一种利用大语言模型与迭代优化技术生成解释的智能代理，其解释体现目的性、因果性和组合性原则。初步评估表明，该方法超越了仅依赖非结构化文本的代理所产生的浅层回答，大幅提升了反馈的深度与相关性。这有助于学习者在在线环境中建立全面的技能理解，从而有效解决问题。
原文摘要 · Abstract (English)
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.
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