An Ontology-Based Approach to Optimizing Geometry Problem Sets for Skill Development
Michael Bouzinier, Sergey Trifonov, Matthew Chen, Tarun Venkatesh, Lielle Rifkin
本文介绍了一种用于注释和组织欧几里德几何问题的本体论和方法,这些问题于20世纪90年代初开发并作为软件工具实施。 虽然这项工作的大部分 - 包括本体和解决方案图范式 - 是在30多年前完成的,但我们认为它在现代人工智能的背景下重新具有相关性。 特别是,我们探讨了这样的假设,即这个既定的框架在与当代大型语言模型配对时可以促进自动化的解决方案验证和反馈,从而支持教师和自学者进行几何教育。 我们记录了原始架构及其持久价值,并概述了将历史教育资源与下一代人工智能技术联系起来的途径。
Euclidean geometry has historically played a central role in cultivating logical reasoning and abstract thinking within mathematics education, but has experienced waning emphasis in recent curricula. The resurgence of interest, driven by advances in artificial intelligence and educational technology, has highlighted geometry's potential to develop essential cognitive skills and inspired new approaches to automated problem solving and proof verification. This article presents an ontology-based fr...