Relocating the Locus of Generation: The AI-GLM Framework for Generative Learning in AI-Mediated Mathematics Education

Abstract Generative learning theory holds that learning depends on the learner’s own construction of relations between prior knowledge and new material, and its evidential practice has long inferred that construction from what the learner produces. External provision of such work is not new, but generative AI extends it across every generative strategy, supplies it on demand, and leaves little trace of its origin, so the locus of generative activity becomes instructionally and empirically ambiguous. Determining who performed the cognitive work that learning requires thus becomes a design and measurement problem. This paper examines that problem in mathematics learning. Through an integrative conceptual analysis of Fiorella and Mayer’s eight generative learning strategies, I identify four cross-cutting themes: the production-to-evaluation shift, metacognitive displacement, the scaffolding–substitution continuum, and reconfigured generative activity. Synthesizing these themes, I propose the AI-Enhanced Generative Learning in Mathematics (AI-GLM) framework, comprising (a) a typology of three AI roles, in which Generation Partner and Generation Substitute occupy the poles of a scaffolding–substitution continuum while Generation Catalyst is distinguished by a functional criterion rather than by a position on it; (b) a role determination mechanism specifying how tool design, task design, learner orientation, and teacher mediation combine to determine the role AI occupies in a given episode; and (c) five design principles. The framework addresses the representational, symbolic, and justificatory demands especially prominent in mathematics and yields propositions stated with the operationalizations and falsification conditions needed to test them.