This study investigates how technology-enhanced tools and Artificial Intelligence (AI) can be used to personalize fraction learning in primary education through cognitive profiling. Fractions are a foundational area of mathematics, yet many students struggle to understand their abstract concepts. While research has shown that cognitive skills such as working memory, attention, and visual perception influence mathematics performance, there is limited evidence on how these factors specifically shape students’ Conceptual Understanding (CU) and Procedural Knowledge (PK) of fractions. To address this gap, senior primary school students in Greece were assessed using the RODI cognitive profiling application, the Fraction Lab learning tool, and additional validated measures of CU and PK. AI-based analyses were applied to examine relationships between cognitive skills, fraction performance, and learning outcomes, and to map students into distinct cognitive profiles. Results demonstrated strong links between visual perception, executive function, and fraction performance. Distinct profiles revealed different patterns of strength and challenge, which were then used to design adaptive learning pathways. The findings highlight how digital tools can provide teachers with actionable insights for tailoring instruction, enhancing engagement, and improving outcomes in fraction learning.
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