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The presence of artificial intelligence (AI) in education is an undeniable reality that affects all participants in the learning process. Geometry, as a subject with specific features and abstract concepts, presents a particularly promising field for exploring the potential of AI to enhance teaching and learning conditions. However, there is a lack of clear methodological guidance for the effective integration of AI in geometry education, highlighting the need for systematic and up-to-date reviews that synthesize current knowledge and provide practical insights for teachers and researchers. This study presents a systematic review of research on AI applications in geometry education conducted between 2023 and 2025. Following PRISMA guidelines, peer-reviewed journal articles published in English during this period were identified, resulting in a final sample of 20 studies. Thematic analysis was conducted to address key research questions, including the most studied topics, active regions of research, effective AI methods, strategies and tools, practical outcomes for teachers, and directions for future investigation. The review indicates that AI in geometry education has attracted global attention, with the most active studies originating from Europe and Asia. Research activity increased notably in 2024. The findings highlight the considerable potential of AI to support student learning through interactive modeling, automated feedback, and visualization of complex geometric concepts. At the same time, effective integration requires that teachers develop awareness and practical skills to maximize AI benefits while avoiding overreliance and superficial learning. The review provides validated methods, strategies, and tools for AI use in geometry education, concrete recommendations for teachers, and examples of practical outcomes from recent studies. These insights offer valuable guidance for educators seeking to implement AI effectively and point researchers toward emerging trends and priorities in improving geometry teaching and learning.
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Матяш et al. (2026) studied this question.
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