NONE 20260803083120611 naun/naun content-registration-form@crossref.org content-registration-form International Journal of Education and Information Technologies 2074-1316 Maria Grigori School of Administrative Economic & Social Sciences University of West Attica, Athens, Greece Klimis Ntalianis School of Administrative Economic & Social Sciences University of West Attica, Athens, Greece Nikos E. Mastorakis Technical University of Sofia Sofia, Bulgaria 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. 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