AI meets Piaget and Vygotsky: A theory-driven approach to fraction learning in Greek lower secondary mathematics

Georgios Polydoros 1 *
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1 University of Crete, Heraklion, GREECE
* Corresponding Author
EUR J SCI MATH ED, Volume 14, Issue 3, pp. 337-358. https://doi.org/10.30935/scimath/18535
Published: 08 May 2026
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ABSTRACT

As artificial intelligence (AI) becomes increasingly integrated into education, its pedagogical application must be guided by established learning theories to ensure relevance and developmental appropriateness. This study investigates the innovative integration of AI-based tools into mathematics instruction, focusing on fractions in a Greek lower secondary school. Drawing on Piaget’s constructivist theory, Vygotsky’s sociocultural theory, and information processing theory, it examines whether AI-supported environments can enhance conceptual understanding, procedural fluency, and engagement. A quantitative quasi-experimental design was implemented over six weeks with 63 seventh-grade students divided into an experimental group (n = 31) and a control group (n = 32). The experimental group received AI-enhanced instruction using DreamBox Learning, Fractions Lab, ChatGPT, and Mathia (Carnegie Learning), while the control group followed the standard curriculum. Instruments included a mathematics conceptual understanding test, a procedural fluency test, and a student engagement questionnaire measuring behavioral, emotional, and cognitive engagement. Statistical tests confirmed significant differences between groups, with the experimental group showing higher performance and engagement. The study’s innovation lies in combining adaptive, interactive, and dialogic AI tools within a theory-driven framework. Implications include guiding educators in selecting AI tools that align with cognitive and socio-constructivist principles, fostering both academic achievement and student engagement. Beyond empirical gains, this study contributes a theory-aligned integration model that operationalizes Piagetian constructivism, Vygotskian scaffolding, and information-processing principles within AI-enhanced fraction instruction, offering a transferable blueprint for research and practice.
Keywords: artificial intelligence, mathematics education, fractions, learning theories, secondary education

CITATION

Polydoros, G. (2026). AI meets Piaget and Vygotsky: A theory-driven approach to fraction learning in Greek lower secondary mathematics. European Journal of Science and Mathematics Education, 14(3), 337-358. https://doi.org/10.30935/scimath/18535

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