Generative AI is already embedded in everyday study behaviour, yet most educational uses remain organised around task completion rather than around the mechanisms that produce durable learning. The result is a recurrent cognitive mismatch: AI improves fluency, speed, and immediate output at precisely the moments when learners should be retrieving, explaining, monitoring, and transferring. This article develops EFFORT-AI (Elicit-Formulate-Feedback-Organize-Reflect-Transfer), a design-based implementation module specifying how AI should be sequenced across learning episodes so that assistance amplifies rather than replaces cognition. The model is built on three premises: preserve target cognition, require bounded productive effort before strong assistance, and adapt support to learner expertise, task element interactivity, and calibration accuracy. EFFORT-AI combines six recursive phases with three cross-cutting control layers: effort preservation, load-adaptive scaffolding, and accountability/AI literacy. Each phase defines the learner's required cognitive act, the permissible role of AI, the activated learning mechanisms, and the principal risk mitigated. Because no original empirical study is reported, validity is argued through theoretical triangulation across cognitive load theory, retrieval practice, desirable difficulties, metacognition, self-regulated learning, schema formation, transfer research, and cognitive offloading, supplemented by emerging evidence from AI tutoring studies. The central claim is that educational AI is most defensible when it removes extraneous friction while preserving retrieval, explanation, monitoring, and transfer as learner-owned processes. The module contributes a phase-sensitive grammar for classrooms, AI learning systems, and policy design, including implications for AI literacy and human oversight under the EU AI Act.
Agnė Diana Liubertaitė (Wed,) studied this question.