From Self-Regulated to Hybrid-Regulated Learning: Reframing Regulation in the Age of AI
TL;DR
It is argued that effective regulation in AI-rich environments depends not only on learners’ strategic abilities but also on their capacity to critically manage and interpret AI support, as well as the critical literacies required to engage with them.
Abstract
Abstract Self-regulated learning (SRL) has long been central to language education, encompassing how learners plan, monitor, and evaluate their learning in pursuit of meaningful goals. The growing integration of artificial intelligence (AI) into language learning environments, however, is reshaping these regulatory processes by distributing aspects of planning, feedback, monitoring, and strategy use across human and technological systems. This article introduces the Hybrid-Regulated Learning (HRL) Framework, a conceptual model describing how learners co-regulate language learning with AI across eight stages: motivation, needs analysis, goal setting, planning, task engagement, monitoring, reflection, and integration. The framework links each stage to specific AI roles (tutor, evaluator, recommender, simulator, and tool), the critical literacies required to engage with them, and the artefacts through which regulatory processes become visible and assessable. Drawing on research in self-regulated learning, AI-supported learning, and language education, the article argues that effective regulation in AI-rich environments depends not only on learners’ strategic abilities but also on their capacity to critically manage and interpret AI support. The HRL framework offers a foundation for understanding and designing language learning environments in which regulatory control is negotiated across human and artificial agents.