## 3 Results
### Adoption and Implementation Challenges: Funding Models, Digital Divide, and Tacher Training
For each VR frame or time step, the data flow is therefore  
\[I\to\text{FaceDetect}(I)\to\text{LandmarkModel}(I,B)\to\text{ ConvexHull}(\dots)\to\text{ erase all raw data.}\]  
Only the polygon vertices \(\mathcal{C}=\{(p_{j}^{x},p_{j}^{y})\}\) remain for any subsequent AI processing (for instance, tracking head orientation). If further anonymization is required, these polygon coordinates may be quantized, for example:  
\[(p_{j}^{x},p_{j}^{y})\mapsto(\lfloor p_{j}^{x}/d\rfloor,\lfloor p_{j}^{y}/d \rfloor),\]  
where \(d\) is a chosen granularity factor. This step restricts the precision and precludes reidentification of the individual. At the network level, if data must be shared beyond the local device or edge server, only \(\mathcal{C}\) in its quantized form is transmitted via 5G, avoiding any transmission of unprocessed or high-resolution biometric frames. Periodic secure erasure of stored contours, in accordance with institutional privacy policies, further reduces the chance of data leaks or unauthorized reconstruction of sensitive features. By embedding these actions into the VR pipeline, it becomes possible to deliver personalized or adaptive content while avoiding the persistent capture of facial images or similarly identifying biometric data. Lastly, a growing consensus acknowledges the moral and ethical implications of continuous digital surveillance in classrooms. Although sophisticated analytics offer real-time detection of confusion or stress, these same systems can infringe on privacy and student autonomy [38]. Some frameworks address these risks by incorporating \"privacy by design\" principles, requiring that only essential, de-identified metrics are stored or analyzed. In practice, however, finding the right balance between safeguarded data collection for educational gains and safeguarding personal rights demands ongoing dialogue among educators, policy-makers, parents, and students themselves [65]. In a recent SWOT analysis conducted among Bulgarian university students, ref. [66] identified clear patterns in student attitudes toward generative AI tools. High adoption  
Figure 4: The outlined proposed workflow of a VR STEM class guarantees a manageable long-term system alongside strong data security while effectively incorporating AI-driven real-time updates within a 5G-enabled VR environment. rates were evident among Information Science (89.1%), Economic Science (80%), and Engineering students (57.4%), contrasting sharply with Library Science students (39.5%). Despite widespread adoption, there remain substantial concerns: approximately half (50%) of Library Science students, and more than one-third (36.4%) of Information Science students, explicitly requested additional training courses on AI due to uncertainty or low trust in the accuracy of generative outputs. Additionally, significant concerns regarding accuracy and ethical implications were voiced: notably, 33.4% of economics students, 17% of engineers, and 15.8% of librarians assessed the impact of generative AI negatively or rather negatively. These empirical findings further substantiate the need, identified in our study, for targeted AI training initiatives and rigorous ethical oversight in educational contexts. Incremental adoption models can also prioritize minimal viable connectivity and progressively scale up. For instance, ref. [13] illustrates how partial AI tutoring solutions can be tested first in blended-learning contexts before attempting district-wide VR laboratories or full-scale 5G upgrades. Coupling these phased deployments with open source education platforms allows schools to evaluate network load, user satisfaction, and security demands in real time, reducing the financial risk of an all-at-once rollout. In tandem, pilot programs which offer micro-credentialing for teachers who master the new digital tools can give a sense of ownership and continuous improvement among faculty.