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This paper introduces the Agency Allocation Framework (AAF) to analyze how decision-making authority is distributed between learners, educators, institutions, and AI systems in AI-mediated learning environments. The AAF examines the distribution of decisions, choice architecture, supporting evidence, and the time horizons of consequences. Through a literature review and tutoring-system example, the authors identify four challenges in studying learner agency at scale, including conceptual ambiguity and trade-offs between efficiency and control.
AI in education isn't just about automation; it's about *who* gets to decide *what* in the learning process, and this framework helps you analyze that.
As AI-mediated learning systems increasingly shape how learners plan, decide, and progress through education, learner agency is becoming both more consequential and harder to conceptualize at scale. Existing research often treats agency as a proxy for engagement and self-regulation, leaving unclear who actually holds decision-making authority in large-scale, automated learning environments. This paper reframes learner agency as the allocation of decision authority across learners, educators, institutions, and AI systems. We introduce the Agency Allocation Framework (AAF) for analyzing how decisions are distributed, how choices are architected, what evidence supports them, and over what time horizons their consequences unfold. Drawing on a focused review of Learning@Scale literature and an illustrative tutoring-system example, we identify four recurring challenges for studying learner agency at scale: (1) conceptual ambiguity, (2) reliance on behavioral proxies, (3) trade-offs between efficiency and learner control, and (4) the redistribution of agency through AI-mediated systems. Rather than advocating more or less automation, the AAF supports systematic analysis of when AI scaffolds learners'capacity to act and when it substitutes for it. By making decision authority explicit, the framework provides researchers and designers with analytic tools for studying, comparing, and evaluating agency-preserving learning systems in increasingly automated educational contexts.
CMU ML