Who Decides in AI-Mediated Learning? The Agency Allocation Framework

TL;DR

This paper introduces the Agency Allocation Framework (AAF), a tool for analyzing how decision-making authority is distributed among learners, educators, institutions, and AI in automated learning environments.

Contribution

The paper presents the AAF as a novel framework to systematically analyze and compare agency distribution in AI-mediated learning systems.

Findings

Identifies four key challenges in studying learner agency at scale.

Highlights the importance of explicit decision authority analysis.

Provides a case example illustrating the framework's application.

Abstract

As AI-mediated learning systems increasingly shape how learners plan, make decisions, 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 at 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.