Self-regulated learning is essential for developing lifelong learning skills, critical thinking, and academic resilience. In mathematics, self-regulation skills are particularly important: For example, when learners work independently on tasks during self-directed learning phases without immediate support from the teacher.
Adaptive digital AI tools offer both opportunities and challenges for the development of self-regulation skills: on the one hand, they may undermine students’ ability to regulate their own learning by increasing dependence on algorithmic guidance; on the other hand, they may strengthen self-regulatory skills through personalized support. With respect to mathematics learning, AI tools create the opportunity to provide elaborated mathematical feedback, address learners’ specific misconceptions, and adapt tasks to their level of understanding and interests.
Against this background, a central question emerges: How much, and what kind of adaptive support do individual students need, compared to independent self-regulation, during different phases of mathematical learning?
