The safety gap: restoring productive struggle through pedagogically aligned generative AI

Abstract

The integration of Generative Artificial Intelligence (GenAI) into education presents a paradox: the more helpful the tool, the more it risks eroding the cognitive processes essential for deep learning. Educational research has long shown that meaningful learning depends not only on correct answers, but also on productive struggle, schema construction, and forms of scaffolding that preserve rather than bypass learner effort. This challenge is especially acute for novice learners, whose understanding is still developing and for whom premature answer delivery may replace the generative processes through which durable knowledge is formed. This paper introduces the concept of the “Safety Gap”—the divergence between a student's AI-assisted performance and their internal capability to verify that output, leading to potential epistemic risks when the tool fails. While GenAI offers unprecedented opportunities for personalized support and content generation, current Oracle-style models often eliminate the productive struggle necessary for schema construction. Drawing on recent literature, we argue that educational AI should be designed not merely to maximize convenience or answer completion, but to support cognitive engagement and independent judgment. We therefore propose a pedagogical framework centered on Socratic and Adversarial AI architectures that withhold direct solutions, introduce constructive cognitive friction, and prioritize process-based assessment over final outputs.