## 4 Discussion
### Sustainability and Long-Term Viability  
A final overarching concern is the environmental and financial sustainability of high-density 5G infrastructures paired with AI's computational intensity. Ref. [9] point out that the energy consumption of next-generation networks can be substantial if systems are not carefully optimized. Similarly, repeated hardware refresh cycles (e.g., upgrading VR headsets, IoT sensors, or edge servers) risk exacerbating electronic waste. At the same time, AI can automate power management, enabling sleep modes for idle devices and dynamic workload balancing and conserving resources. As [28] showed, 5G-powered VR in education leverages ultra-low latency (\(\leq\)1 ms), high-speed data transmission (up to 10 Gbps), and network slicing to enable real-time, immersive learning environments. VR applications require at least 100 Mbps per user, which 5G efficiently provides using massive MIMO and edge computing, reducing data transmission costs by 25% and latency-related motion sickness by 40%. In their survey, 77.39% reported occasional network instability. The study also found that optimized caching strategies improved FoV coverage by 30%, enabling smoother 4K/8K VR streaming for AI-driven interactive simulations. However, high costs of VR headsets exceeding USD 500 per unit and inconsistent 5G infrastructure remain barriers to widespread adoption, emphasizing the need for cost-effective solutions and targeted educator training to fully exploit 5G-VR integration in education, as 90% were aware of VR's potential in education or used it. On the other hand, network slicing, wherein individual virtual network segments cater to specific applications or classes with high bandwidth, could support an immersive language laboratory which requires real-time audio-visual synchronization. Alternatively, a more latency-tolerant slice could handle asynchronous content uploads for remote learners. In this way, by dynamically allocating resources based on predictive analytics, schools can prevent congestion and maintain consistent performance during peak usage periods [71]. Adoption strategies which prioritize scalable designs and carbon-footprint monitoring can mitigate these environmental costs. Detailed network protocols [30], including LoRaWAN, ZigBee, and 6 LoWPAN, optimize connectivity with IPv6 for low-power devices. LoRaWAN supports over a million devices per network with ultra-low power consumption, while LTE-A improves data rates through OFDMA. Additionally, it is shown that MQTT enhances real-time IoT communication, too, and CoAP, which utilizes a server-client model, can reduce bandwidth consumption, improving efficiency in constrained networks. Additionally, local governments are increasingly interested in green AI benchmarks, emphasizing energy-efficient algorithms, hardware longevity, and circular economy principles when evaluating educational technology proposals [72]. The decentralized, efficient energy distribution, enabled by blockchain's transformative role in Smart Grid Demand Response (DR), could also be leveraged [73]. In AI-driven educational settings, blockchain frameworks could be utilized to optimize energy use in 5G-powered classrooms. By leveraging blockchain-based smart contracts, peer-to-peer energy trading and real-time load balancing, educational institutions can implement real-time energy adjustments. Thus, dynamically distribute energy to 5G towers and AI computing hubs, ensuring minimal energy wastage. This way, sustainable power consumption for AI and IoT-driven learning tools can be achieved. To ensure that emerging 5G-AI ecosystems remain viable, a socio-technical approach is appropriate, which incorporates cultural, psychological, and community-specific factors. In contexts where large classes pose systemic challenges, educators can harness real-time analytics for micro-group interventions, thus mitigating the burden on individual instructors [62].