## 3 Results
### AI-Driven Security and Privacy Frameworks with Edge Computing  
While these personalized frameworks have bolstered motivation and performance, algorithmic biases or excessive reliance on AI-based tracking tools may undermine student autonomy. Concerns also arise about teachers being sidelined by generative AI assistants, which can automatically prescribe lesson content, limiting pedagogical spontaneity [4]. Hence, balancing teacher agency with AI-driven automation is crucial to maintaining student engagement and academic freedom. Human insight combined with adaptive AI can yield promising outcomes, especially when instructors maintain oversight and adjust AI recommendations according to each learner's emotional, cultural, and cognitive states [39]. As the number of connected endpoints increases in a 5G-enabled classroom, cybersecurity and privacy concerns grow proportionally. Cybercriminals target educational networks due to the abundance of sensitive personal data and relatively weaker safeguards compared to corporate settings like denial-of-service (DDoS), phishing, and ransomware attacks. For example, in England, 96 out of 185 surveyed schools confirmed an attack or a breach, as shown by [48]. Because massive volumes of personally identifiable information \begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**VR Type** & **Key Features** & **Sample Educational Uses** \\ \hline Non-Immersive (Desktop) & Uses a standard PC or laptop, low sense of presence, pros: low cost, cons: limited immersion & Basic 3D simulations on a monitor, desktop-based virtual worlds, e.g., simple physics or math lessons \\ \hline Semi-Immersive (Projection) & Large screens or domes, moderate immersion, pros: good for groups, cons: higher cost & Geometry lessons with a power wall, vocational flight simulators, collaborative demonstrations in class \\ \hline Fully Immers (HMD/CAVE) & VR headsets or CAVE rooms, high presence, pros: natural gestures, cons: cost and potential motion sickness & Advanced virtual laboratories, interactive biology or chemistry modules, CAVE-based engineering or design tasks \\ \hline \hline \end{tabular}
\end{table} 
Table 5: VR classification for education. (PII) and potentially revealing biometric data flow through IoT devices and AI tools, the stakes for heightened security are important. In response to attacks, schools, like businesses, increasingly deploy AI-driven Intrusion Detection and Prevention Systems (IDPSs), which recognize suspicious patterns in real time [49; 50]. For instance, ref. [51] introduced a 5G cyber range simulation where trainees learned to mitigate distributed DDoS attacks using an AI-enhanced detection framework. In parallel, ref. [52] demonstrates how hybrid end-to-end encryption schemes combining AES-256 with elliptic-curve cryptography strengthen data secrecy, thus mitigating man-in-the-middle exploits when implementing IoT systems in SDN contexts. However, these encryption protocols can complicate legitimate monitoring needs such as AI-based e-protoring of exams or content moderation [53]. Moreover, as big data applications grow in scale, so do privacy and computing challenges, especially in cloud computing environments [54]. When educational institutions adopt AI analytics, they must manage large-scale data storage and processing, which often exceeds the capabilities of existing cloud infrastructure. Beyond core encryption and intrusion detection, zero-trust architectures and RBAC frameworks offer systematic means to limit data exposure [55]. A zero-trust model enforces continuous authentication, denying automatic access even for internal traffic. By incorporating zero-trust micro-segmentation, the ability to confine IoT breaches to localized areas of the network is achieved [23]. This prevents lateral movements, which could compromise the entire educational system.