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Technical paper presents a conceptual framework for Mixture of Experts in education, suggesting a novel analogy for understanding.
This technical paper presents a novel conceptual framework for understanding the Mixture of Experts (MoE) architecture in Large Language Models (LLMs) through a comprehensive academic administration analogy. By mapping routers, experts, and gating networks to university offices and specialist faculty, this bilingual (English/Turkish) manuscript bridges the gap between complex deep learning mechanisms and accessible educational concepts. It aims to serve as a foundational resource for independent AI researchers and educators globally.
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Musa Gülmak (2026) studied this question.
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