## 6 Future Outlook and Recommendations  
### Toward Sustainable AI Integration  
As educational data expand exponentially, emerging frameworks call for more energy-efficient AI models and extended device lifespans. Hybrid approaches combining 5G with low-power networks (e.g., LoRaWAN) may reduce carbon footprints, while solar-powered base stations or energy-aware scheduling algorithms can curb operational costs. Tools such as explainable AI and transparency dashboards will also help administrators and teachers oversee automated processes without exacerbating resource usage. A scenario-based approach can also help policy-makers envision how advanced technologies shape future employability skills, as [79] proposes by reimagining higher education pathways for workforce readiness in an ever-evolving digital era. Prescriptive maintenance frameworks which combine descriptive and predictive analytics have already shown that they can enhance system reliability in small-scale settings [80]. Integrating this approach into 5G-enabled smart classrooms allows AI-driven analytics to detect faults, optimize resource allocation, and ensure sustainable, autonomous operation of educational buildings and their infrastructure.  
Apart from immediate energy costs, educational stakeholders increasingly consider the full lifecycle impact of digital devices, from manufacturing to disposal [10]. In a circular economy model, for example, outdated VR headsets, IoT sensors, and edge servers could be refurbished and repurposed for lower-intensity tasks rather than discarded. Schools experimenting with \"device rotation\" or refurbishment programs claim notable reductions in e-waste and increases in cost savings of hardware budgets. Aligning procurement policies with green policy standards can thus mitigate the ecological footprint of perpetual technology upgrades, preserving the sustainability gains delivered by AI-based optimizations. Similarly, AI-driven telecommunications in smart classrooms can significantly benefit from predictive analytics. They can enhance network adaptability and resource allocation. Research on short-term load forecasting shows that optimizing machine learning models for real-time energy prediction improves system efficiency and responsiveness to dynamic demand [81]. This approach proves the need for data-driven methodologies in telecommunications to ensure adaptive and resilient infrastructure in order to create intelligent and responsive educational environments.  
Consideration should also be given to the role of the broader community, local businesses, and civic organisations in shaping a sustainable AI education ecosystem. By involving community voices in design reviews and pilot feedback sessions, schools en sure that the rollout of 5G and AI respects local cultural norms without disturbing public trust [16]. Such participatory methods can also help identify local-level solutions for technology maintenance and cost-sharing, enhancing both longevity and societal acceptance. Ultimately, addressing sustainability in education technology transcends any single initiative, demanding ongoing collaboration which balances economic feasibility with environmental stewardship.