Afet Akademi

Traditional disaster education relies on static instruction and declarative knowledge, which fails to model real-time decision-making under uncertainty. There is a need for immersive systems that can measure hesitation, contextual transfer, and adaptive behavioral calibration in high-stakes environments.

Afet Akademi integrates immersive simulation with Bayesian Knowledge Tracing (BKT) and stealth assessment principles. The system models learner knowledge states in real time, tracks hesitation duration, and adapts scenario complexity dynamically. A Socratic AI mentor (BİLGE) guides reflective thinking without providing direct answers, supporting metacognitive engagement and contextual knowledge transfer between home and school scenarios.

The system demonstrates a structured framework for modeling behavioral calibration in disaster preparedness education. By embedding assessment invisibly within gameplay, Afet Akademi shifts evaluation from right/wrong scoring to cognitive state inference. The project establishes a foundation for adaptive immersive learning infrastructures and explainable learning analytics integration.