Simplifying Complex Wireless Intelligence and Security Education: An Outcome-Based Blended Learning Approach for Mobile Engineering

Abstract

The growing complexity of wireless communication systems and mobile security threats demands a new generation of engineers capable of operating at the intersection of intelligent wireless infrastructure, software-defined radio (SDR), and mobile AI. This paper proposes an intelligent mobile security education platform that integrates AI-driven learning analytics, SDR-based practical interfaces, and cloud-based wireless simulation environments within an Outcome-Based Education (OBE) and Cognitive Load Theory (CLT) framework. The platform transforms passive learners into active creators of wireless security content via Student-Generated Multimedia (SGM), while AI dashboards monitor cognitive readiness and adapt task difficulty in real time. Inclusive design extends platform accessibility through wearable biometric sensors and AR-assisted visualization of 3D wireless signal data. A pilot study was conducted with two student cohorts ($n = 13$ per group) to evaluate the SGM component of the platform within a blended learning delivery model. The active multimedia group achieved a 76.9\% pass rate (10/13) against 23.1\% (3/13) in the traditional control group, with a statistically significant and large effect ($t(24) = 3.28$, $p = 0.003$, Cohen's $d = 1.29$). These results provide preliminary evidence that the platform's intelligent, active-learning approach substantially improves training outcomes for mobile wireless security engineering. Full empirical validation of the AI analytics, AR/VR laboratory, and 5G/IoT edge-computing modules is planned for future work.