Teaching Geometry Concepts Using Artificial Intelligence: Algorithms, Spatial Visualization, and Interactive Problem Solving

This paper presents a comprehensive framework for integrating Artificial Intelligence (AI) into geometry education, covering algorithms, spatial visualization, and interactive problem solving. We demonstrate how Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Reinforcement Learning (RL), and hybrid neuro-symbolic methods can be employed to support students' conceptual understanding of plane and solid geometry. The framework connects symbolic reasoning with data-driven learning to produce stepwise solutions, intelligent hints, and adaptive tasks. We implement examples using GeoGebra for dynamic constructions, Unity for ۳D interaction, Rhino/Grasshopper for procedural modeling, and AR/VR toolchains for immersive learning. Results from classroom-style scenarios suggest improved spatial reasoning, increased engagement, and higher problem-solving accuracy compared with traditional instruction. We further analyze cognitive load and trace student-system interactions to design formative feedback policies. Finally, we discuss the rise of systems such as AlphaGeometry and large language models (LLMs) and outline an agenda for robust, ethical, and explainable AI in mathematics education.

کلیدواژه ها:

Artificial Intelligence ، Geometry Education ، Spatial Visualization ، Interactive Learning ، Reinforcement Learning ، Computer Vision ، Neuro-Symbolic Reasoning