A Community Science Journal for Educational Systems Improvement
Our editorial board
Our community science journal aims to be a unique publication, bridging academic rigor with community-based application. Our approach works towards integrating machine learning (i.e. AI) and data science methods in qualitative content analysis, with novel approaches to participatory educational systems improvement.
Our editorial board is composed of diverse academic, professional, and youth leaders.
Connect with us if you are interested in applying to serve on the editorial board.
Youth mentors (18+, university-based or other expert role)
Youth leaders (16-18, school or other learning environments)