## 3 Results
### Adoption and Implementation Challenges: Funding Models, Digital Divide, and Tacher Training  
Although the advantages of 5G- and AI-enabled learning are evident, scaling these systems institution-wide poses multiple hurdles. Financial constraints rank high among them, as adopting high-density antenna arrays, specialised VR gear, and strong AI platforms can exhaust school budgets. Where philanthropic grants or state subsidies may support pilot programs, sustaining and expanding them typically requires long-term funding and skilled technical staff. Infrastructure readiness is another key bottleneck. Many educational institutions lack reliable broadband backbones, especially in remote areas. Without consistent high-speed connectivity, advanced AI or VR modules degrade into sporadic or laggy experiences [32]. Schools may resort to phased rollouts, which begin with partial 5G coverage targeting laboratories or specialized classrooms and gradually extend coverage to the entire campus [59]. Others incorporate alternative design strategies like mobile edge micro-centers or caching servers to mitigate limited network bandwidth. On the human capital side, teachers must receive comprehensive professional development to make full use of real-time data dashboards, AI tutoring engines, and IoT-based feedback loops [60]. In technical disciplines, the complexity of advanced analytics, iterative virtual laboratory updates, and new software platforms, such as SVM-driven frameworks which predict students' \"understanding\" and \"implementation\" rates in engineering courses can initially overwhelm instructors accustomed to traditional pedagogies [61]. Workshops and ongoing training can help demystify AI-driven insights, making them more actionable in day-to-day teaching [62]. Simultaneously, administrators often require guidance in balancing local policy directives such as ethical data governance and e-proctoring standards, due to the rapid pace of technological change [46]. In these contexts, Figure 4 proposes an AI-driven workflow for 5G-based VR laboratory updates (e.g., in STEM classes), ensuring adjustable biometric captures and sustainable maintenance even in the absence of key developers. The system, after the initial setup, can collect user data anonymously in real time and is able to apply on-the-fly AI/ML adjustments.It then proposes deeper improvements and routes them for instructor approval, after which updates are deployed over 5G (Step E). Step F ensures no single-person dependency, with the last two steps concerning the data minimization handling and the continuous monitoring of new metrics. This cycle repeats while steadily refining the VR environment. To address personal privacy concerns, a simple anonymization approach is presented. The face detection step utilizes established facial detection methods, such as Multi-task Cascaded Convolutional Networks (MTCNNs) [63] or facial landmark detectors based on the regression tree algorithm by [64], commonly implemented in Dlib's 68-point predictor. Let \(I\) be an image frame (or a single time slice in the VR session). Let \(\text{FaceDetect}(I)\) return a bounding box \(B=(x,y,w,h)\) indicating the region in which a face is located. A facial landmark model,  
\[\text{LandmarkModel}(I,B)\rightarrow\{(x_{i},y_{i})\}_{i=1}^{N},\]  
then detects key facial points. These landmarks can be reduced to a minimal set of coordinates by computing the convex hull,  
\[\mathcal{C}\ =\ \text{ConvexHull}\big{(}\{(x_{i},y_{i})\}\big{)},\]  
which provides a polygonal approximation of the outer face contour. In this manner, raw image details or full facial textures are never stored. Once the polygon \(\mathcal{C}\) is formed, the original frame \(I\) and any intermediate landmark sets are erased from memory, ensuringthat no recoverable biometric information persists.