Challenge Area:
Personalized learning in under-resourced schools.

Problem Statement:
Students in low-income schools lack tailored educational support, leading to high dropout rates.

Solution Concept:
Build an AI-based adaptive learning platform that analyzes student performance data to deliver customized lesson plans and real-time feedback for teachers.

Expected Impact:
Improve pass rates by 25% for 1,000 students in pilot schools.

Next Steps:
Develop machine learning models for learning analytics, create a web-based platform, and collaborate with local education boards.

Challenge Area:
Educational Equity

Problem Statement:
Students in underfunded schools lack personalized learning resources, leading to achievement gaps.

Solution Concept:
Develop an adaptive learning platform using AI to recommend customized study materials based on student performance data, employing a collaborative filtering algorithm. The PoC includes modular data pipelines for student data and performance metrics for scalability.

Expected Impact:
Improves student performance by 15% in underserved schools, reaching 50,000+ students.

Next Steps:
Add gamification features, support offline access, and integrate with open educational resources.