What question did this study set out to answer?

The aim is to create a framework that integrates AI into self-regulated learning without undermining learner autonomy.

April 19, 2026Open Access

AI as a co-regulator: relational design for strengthening self-regulated learning

Puntos clave

The aim is to create a framework that integrates AI into self-regulated learning without undermining learner autonomy.
Proposed a unified model of AI functioning as a co-regulator in self-regulated learning.
Grounded the model in Winne and Hadwin's COPES architecture.
Identified five design principles for AI's role in supporting regulatory cycles.
Highlighted the concept of productive metacognitive friction to sustain learner-driven regulation.
Established evaluative priorities like autonomy and interpretability for AI in educational settings.
Outlined implications for practice, workplace learning, equity, and governance.

Resumen

The growing presence of artificial intelligence (AI) across educational and workplace environments is reshaping how learners encounter tasks, interpret feedback, and navigate uncertainty. To understand these changes, this manuscript grounds AI's influence in theories of self-regulated learning (SRL), which conceptualize learning as a cyclical process of planning, monitoring, strategic adjustment, and reflection. Rather than replacing these processes, AI reshapes the conditions under which they occur by making some cues more visible, introducing new forms of guidance, and occasionally preempting difficulty before learners have an opportunity to engage with it. These shifts reveal a conceptual gap: although research documents both benefits and risks of AI-mediated support, we lack a framework for understanding how AI participates in learners' regulatory cycles across educational and professional settings without eroding the autonomy that underpins SRL. To address this gap, this article proposes a unified model of AI as a co-regulator within self-regulated learning, grounded in Winne and Hadwin's COPES architecture. The model centers productive metacognitive friction as a mechanism for sustaining learner-driven regulation by structuring how learners encounter challenge and discrepancy. It advances a relationally grounded framework at the level of interactional structure, positioning AI as a co-regulator through five design principles that specify conditions under which AI can support regulatory cycles without displacing learner judgment. These principles are linked to an evaluation architecture that centers autonomy, interpretability, process integrity, and developmental growth as evaluative priorities traced through learner–AI interaction patterns. Implications are examined across educational practice, workplace learning, equity, and governance, and directions for collaborative research and design are outlined to investigate how relationally aligned AI can preserve and strengthen the regulatory processes at the heart of SRL.

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