专家知识能提升教学系统效果，助力自动生成可解释课程。
On the Role of Domain Experts in Creating Effective Tutoring Systems
- 用专家规则结合新型可解释AI技术自动生成教学内容。
- 专家制定的课程体系让自适应教学系统更高效。
- 案例验证：专家知识易获取，适合生态识别类教学系统。
在人工智能教育领域，来自领域专家的高度结构化知识常被忽视。本文通过两个途径探讨其价值：一是利用专家指定的解题规则与新型可解释AI（XAI）技术，自动构建可向学习者提供的教学课程；现有大多数XAI方法主要用于调试AI系统，而本研究拓展其应用至课程生成。二是基于专家设计的学习目标课程体系，可支持开发更高效的自适应教学系统，提升学习体验并优化算法效率。最后，以授粉者识别教学系统为例进行案例研究，证明此类专家知识易于提取，且能有效支撑系统构建。
原文摘要 · Abstract (English)
The role that highly curated knowledge, provided by domain experts, could play in creating effective tutoring systems is often overlooked within the AI for education community. In this paper, we highlight this topic by discussing two ways such highly curated expert knowledge could help in creating novel educational systems. First, we will look at how one could use explainable AI (XAI) techniques to automatically create lessons. Most existing XAI methods are primarily aimed at debugging AI systems. However, we will discuss how one could use expert specified rules about solving specific problems along with novel XAI techniques to automatically generate lessons that could be provided to learners. Secondly, we will see how an expert specified curriculum for learning a target concept can help develop adaptive tutoring systems, that can not only provide a better learning experience, but could also allow us to use more efficient algorithms to create these systems. Finally, we will highlight the importance of such methods using a case study of creating a tutoring system for pollinator identification, where such knowledge could easily be elicited from experts.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。