S-A I-EDU: A Bio-Inspired and Modular Sparse AI Architecture for Adaptive and Symbolic Intelligent Educational Systems

This paper introduces S-AI-EDU, a bio-inspired, modular, and parsimonious AI architecture designed for adaptive and symbolic intelligent educational systems. Unlike data-centric black-box models, S- AI-EDU employs hormonal modulation and symbolic agent orchestration to adaptively select pedagogical, motivational, and evaluation agents while maintaining transparency, interpretability, and cognitive economy. The architecture integrates a MetaAgent for global orchestration, specialized educational agents for targeted interventions, and Gland Agents simulating artificial educational hormones (e.g., Curiosin, Confusionin, Attentionin, Fatiguin, Dopaminin) to dynamically regulate instructional intensity. A symbolic memory module preserves learner paths, misconceptions, and engagement patterns, enabling explainable trace-based pedagogy. Experimental validation in simulated learning scenarios over 120 instructional cycles demonstrates the system’s capacity to: 1. Detect cognitive instability (confusion, disengagement) early; 2. Activate only the necessary agents to avoid cognitive overload; 3. Produce interpretable learning traces for pedagogical review. S-AI-EDU offers a resource-aware, emotionally adaptive, and ethically aligned approach to intelligent education, bridging symbolic reasoning with neuro-inspired regulation.