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AI-Powered Network Optimization for Better School Connectivity

Lace

Haven_Shimmero41w

jessica

Hamza_Hassaan

malimalik

Many schools in remote and underserved regions experience high latency due to distant servers and limited bandwidth capacity, which disrupts digital learning. Our solution applies clustering algorithms (K-Means, DBSCAN) to 2.3 million school geolocations, pinpointing where data centers can most effectively serve 90% of educational content while minimizing latency. Leveraging open-source AI tools (Scikit-learn, Streamlit) and real-time connectivity data from Giga, our approach provides actionable insights for governments, NGOs, and internet service providers. In a user-friendly dashboard, stakeholders can visualize schools’ connectivity issues, compare bandwidth savings across regions, and analyze predicted performance boosts from new data centers. We plan to expand our model globally, ensuring accessible, high-quality internet connections for all schools—and ultimately bridging the educational digital divide.


Our AI-driven platform identifies optimal data center sites, drastically reducing latency and bandwidth costs for over two million underserved schools worldwide.

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