DESIGNING ADAPTIVE LEARNING LOGIC
ABOUT AAL
The project was developed as part of the Israeli Ministry of Education’s “720” initiative, focused on advancing adaptive learning. The goal was to design a system that lets educators define learning paths based on student progress, without making the setup feel technical or fragile. Instead of jumping straight into AI, we built a manual foundation first: tracks, segments, conditions, and content logic that educators could understand, edit, and trust. This made the system flexible enough for different learning scenarios, while still keeping the structure clear.
Part of the Ministry of Education's 720 initiative →WHY THIS PROJECT
This project highlights my ability to turn abstract learning logic into a clear product structure. What I liked about it was the challenge of making something invisible feel understandable. Tracks, segments, conditions, and content relationships could easily become overwhelming, and I enjoyed shaping them into a system that could support teachers today and future AI-driven personalization later.
STARTING WITH STRUCTURE
To support adaptive learning, the system needed a foundation educators could actually work with. Instead of relying on automation first, I designed a manual structure that let educators define levels, build segments, add rules, and connect content in a visible way. In most cases, the process starts by defining learning tracks and the relationships between them. But the system also supports a messier real-life flow: creating content first, then connecting it into a structured path later. That flexibility helped different working styles fit into one consistent system.
One of the core challenges was making adaptive learning feel usable. Educators needed a clear way to build multiple learning paths, define how students move between them, and still understand what they had created.