Architecture-Driven Logic Design Strategies for Distributed Educational IoT Environments with Robotic Support

Abstract

The integration of artificial intelligence components and humanoid robotic platforms into adaptive educational software ecosystems requires clear architectural planning, secure data flows and reliable interfaces between user-facing applications, AI services, databases and robotic interaction modules. This paper proposes a formal architecture-driven framework for adaptive educational IoT environments with robotic support. The proposed architecture is represented as a directed graph, where nodes correspond to software, AI, data, security, and robotic components, while edges represent controlled data flows and communication interfaces. This model introduces metrics for end-to-end latency, communication overhead, reliability, traceability, and risk exposure. A small-scale simulation-based validation is presented for evaluating representative learning scenarios involving AI-based personalization, teacher supervision, secure logging, and humanoid feedback robots such as the QTrobot RD-V2 i7 and NAO. This proposed framework supports students with special education needs by combining predictable interaction, multimodal feedback, emotional literacy support, teacher control, and GDPR-compliant data management.