Scaffolding the Scaffold: Co-Evolving Human-Centered Boundaries for GenAI in Education

Abstract

Current educational approaches often treat GenAI access as binary, permit or prohibit, failing to accommodate learning’s developmental nature. This one-size-fits-all approach risks either depriving students of valuable learning opportunities or enabling over-reliance that undermines skill development. While recent work explores instructor-configurable GenAI, no approach treats governance as an adaptive per-student scaffold based on demonstrated competency. We propose co-evolving boundaries: GenAI constraints that evolve dynamically with student competency. Grounded in pedagogical theories (Zone of Proximal Development, scaffolding, mastery learning) and technical capabilities (learning analytics, configurable GenAI), adaptive boundaries would evolve through three developmental stages negotiated among students, teachers, and GenAI systems: restrictive scaffolding for novices, selective access with over-reliance monitoring for intermediates, and open collaboration for advanced students. We present four research questions addressing boundary negotiation, competency detection, transparency, and evaluation. This transforms GenAI from a policy concern into a human-centered pedagogical instrument, enabling co-evolution of AI constraints and student competency while balancing personalization with equity, transparency with usability, and autonomy with guidance.