Optimizing Geometry Problem Sets for Skill Development

This article describes an ontology and methodology for annotating and organizing Euclidean Geometry problems, developed in the early 1990s and implemented as a software tool. While the majority of this work -- including the ontology and solution graph paradigm -- was completed over thirty years ago, we argue that it has renewed relevance in the context of modern artificial intelligence. In particular, we explore the hypothesis that this established framework can facilitate automated solution validation and feedback when paired with contemporary large language models, thereby supporting teachers and self-learners in geometry education. We document the original architecture and its enduring value, and outline pathways for bridging historical educational resources with next-generation AI techniques.