A Theoretical AI - Fuzzy Logic Framework for Student Learning Profiles and Differentiated Instruction in Primary Education


Authors: Maria Grigori, Klimis Ntalianis, Nikos E. Mastorakis

Abstract: This theoretical paper presents an AI-supported fuzzy logic framework for classroom grouping and individualized instruction in primary education. The learner profile integrates three dimensions elicited via a chatbot using a three-point scale: VARK, learning preferences, and interests. Student responses are mapped to fuzzy membership values to construct a compact learner vector enabling dynamic, non-rigid classification. A Mamdani inference mechanism aggregates IF–THEN rules to generate graded recommendations across five pedagogical approaches: Project-Based Learning, Challenge-Based Learning, STEAM, Makerspace/Experiential–Collaborative Learning, and Game-Based Learning. Based on these recommendations, a grouping module supports the formation of heterogeneous or homogeneous student teams aligned with instructional goals. A simulation across varying class sizes indicates that the framework maintains low grouping time while preserving higher group-quality indicators compared to random and manual grouping strategies. The main contributions include a lightweight learner-profile model, fuzzy pedagogical recommendations, and a flexible grouping workflow complementing teacher judgment.

Pages: 159-164

DOI: 10.46300/9109.2026.20.17

International Journal of Education and Information Technologies, E-ISSN: 2074-1316, Volume 20, 2026, Art. #17

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