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

2026-04-15

AI Summary

This study addresses the erosion of learner agency in large-scale AI-supported learning environments, often stemming from ambiguous allocation of decision-making authority. It proposes the Agency Allocation Framework (AAF), which reconceptualizes learner agency as a problem of distributing decision rights among multiple stakeholders—learners, educators, institutions, and AI systems. The framework systematically examines agency dynamics through four dimensions: decision-makers, choice architecture, evidentiary basis, and temporal impact. Drawing on a literature review and empirical cases from the Learning@Scale domain—such as intelligent tutoring systems—the work identifies four key challenges in evaluating agency and offers researchers and designers a practical analytical toolkit to foster the development of AI-powered educational systems that more effectively respect and support learner autonomy.

Technology Category

• Multiagent Systems: Agent/AI Theories and Architectures
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Learning Human Values and Preferences

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

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.

Problem

• learner agency
• decision authority
• AI-mediated learning
• educational automation
• agency allocation

Innovation

• Agency Allocation Framework
• learner agency
• AI-mediated learning
• decision authority
• educational automation