Team 5’s Accra-Flow, a smart system designed to alleviate traffic congestion and improve urban resilience in Greater Accra, was adjudged the overall best project at the Ashesi Microsoft Case Study Competition. Padi, a personalized multimodal AI tutor for university students, secured Team 2 the Impact Award. This award recognized the team that demonstrated the strongest balance between impact and feasibility.
Bringing together students from multiple disciplines, the competition required participants to identify pressing real-world problems and develop solutions leveraging Microsoft AI tools. The focus areas spanned youth employment and small business growth, primary healthcare access, agricultural supply chain resilience, education quality and access, and urbanization and infrastructure resilience.
With many drivers choosing routes without real-time traffic information, and traffic lights treating all vehicles the same regardless of how many people they carry, commuting in Accra often becomes slow and unpredictable. This leads to unnecessary congestion and longer travel times for everyone on the road.
Accra-Flow was proposed to tackle this by giving drivers live traffic updates, allowing traffic lights to prioritize vehicles like minibuses that carry more passengers, and providing commuters with more reliable travel time estimates. It works through smart roadside cameras that analyze traffic while protecting privacy, intelligent traffic lights that learn and coordinate with each other, and simple USSD alerts that communicate this information to commuters.
For Team 2, the starting point was the growing gap in personalized academic support for university students in Ghana. Their solution, Padi, delivers lessons in multiple formats—visual, audio, video, and text—tailored to individual learning preferences. Following a short onboarding process, the system adapts content and explanations to suit each student, revisiting concepts in different ways until mastery is achieved. In doing so, it moves beyond the limitations of one-size-fits-all teaching models.




