Augmenting, not outsourcing: Leveraging artificial intelligence to support students’ self-regulated learning

Self-regulated learning (SRL) is defined as a goal-driven process in which students monitor their learning and enact purposeful regulatory practises in response to obstacles that hinder their goal attainment in learning tasks. While artificial intelligence (AI) can facilitate the completion of learning tasks, it may also outsource students’ SRL processes. In this article, we introduce how the Trigger Regulation Framework can guide the design of AI-enhanced support that augments students’ SRL. Triggers for regulation are events when students experience an obstacle in their cognitive, emotional, or motivational state. We present two cases implemented in secondary classrooms that apply the Trigger Regulation Framework and AI capabilities to invite students to self-regulate their learning. We explain how different data streams and analytics help both trace and support SRL in individual learning tasks and socially shared regulation of learning (SSRL) in collaborative learning tasks. After discussing the potential of these cases to support teachers in AI-enhanced classrooms, we conclude by considering how the opportunities and pitfalls of human-AI interaction can be addressed when designing support for students’ SRL.