AN INTELLIGENT GAME-BASED LEARNING ARCHITECTURE BASED ON AN AI–IOT FRAMEWORK FOR SMART LEARNING ENVIRONMENTS
DOI:
https://doi.org/10.47701/fgjkjy50Keywords:
Artificial Intelligence, Internet of Things, Game-Based Learning, Adaptive Learning, Design Science ResearchAbstract
The integration of Artificial Intelligence (AI), the Internet of
Things (IoT), and Game-Based Learning (GBL) has the
potential to create adaptive learning environments, yet
existing studies rarely combine these technologies within a
unified operational architecture. This study proposes the
Intelligent Game-Based Learning (IGBL) Architecture as the
operational realization of a previously developed AI–IoT
Game-Based Learning framework. Using the Design Science
Research methodology, a five-layer adaptive architecture
was developed and evaluated through expert review and
simulation-based assessment using 80 predefined student
profiles. 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 successful
adaptation rate (80.7% to 88.7%) while maintaining near
real-time processing performance. These findings
demonstrate that the proposed architecture is conceptually
valid and operationally feasible, providing a structured
implementation model for integrating AI, IoT, and GBL in
context-aware adaptive learning environments.
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