<![CDATA[The integration of Artificial Intelligence (AI), the Internet ofThings (IoT), and Game-Based Learning (GBL) has thepotential to create adaptive learning environments, yetexisting studies rarely combine these technologies within aunified operational architecture. This study proposes theIntelligent Game-Based Learning (IGBL) Architecture as theoperational realization of a previously developed AI–IoTGame-Based Learning framework. Using the Design ScienceResearch methodology, a five-layer adaptive architecturewas developed and evaluated through expert review andsimulation-based assessment using 80 predefined studentprofiles. Two architectural configurations were compared:gameplay analytics only and gameplay analytics enriched with contextual learning information obtained through IoT-enabled connected learning technologies. The results showed that the proposed architecture improved learning-state classification accuracy (82.4% to 89.8%), adaptive decision accuracy (79.3% to 86.5%), and successfuladaptation rate (80.7% to 88.7%) while maintaining nearreal-time processing performance. These findingsdemonstrate that the proposed architecture is conceptuallyvalid and operationally feasible, providing a structuredimplementation model for integrating AI, IoT, and GBL incontext-aware adaptive learning environments.]]>
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