Editorial

Self-regulated learning in the age of generative AI: Finding the balance

Abstract

Self-regulated learning (SRL) underpins academic success and lifelong learning, yet remains a persistent challenge for many learners. With the increasing adoption of generative AI (GenAI) in higher education, learners, educators, and researchers find themselves walking a tightrope: how can they balance using GenAI to support human learning while preserving learner agency and epistemic responsibility? This special issue of Learning Letters explores this tension, offering timely insights into the impact of GenAI on SRL. The four featured articles examine how learners self-regulate their learning when using GenAI tools across different tasks and learning contexts, and how GenAI can be leveraged as a dynamic, context-aware SRL support. Together, the studies reveal a recurring tension between efficiency and depth. The editorial synthesises these contributions, identifying two key distinctions: distinguishing cognitive offloading as reduction versus substitution, and assessment for learning versus assessment of learning. Building on these distinctions, two directions for future research are advocated: designing tailored GenAI tools that scaffold SRL and prioritising teaching learners how to self-regulate effectively. Overall, this special issue highlights the core challenge for SRL research: finding the balance between GenAI-enabled support, and the imperative to develop critical, independent learning skills.