The intersection of pedagogy, technology, and product business.

Designing for students, educators, school admins, and IT leads.

Managing trust, transparency, and feedback in AI interfaces.

Running user interviews and low-fidelity prototype testing.

Specifying user stories, acceptance criteria, and ML models.

RICE, Kano, and MoSCoW models applied to educational features.

Conducting sprint planning and wireframe reviews for AI apps.

Managing sprint velocity when training machine learning models.

Testing AI interfaces with real students and teachers.

Tracking DAU/MAU, completion velocity, and feature usage curves.

Designing onboarding loops, trigger notifications, and engagement flows.

Refactoring software codebases and data pipeline debt.

Contracting design agencies and contract ML engineers.

Running live classroom pilot testing and fallback management.

Presenting a comprehensive PRD and functional product prototype.