Teachers' Self-Regulated Learning in Shaping Readiness for AI-Supported Personalized Adaptive Learning in Elementary Education

Abstract

Artificial intelligence offers new opportunities for Personalized Adaptive Learning (PAL), an approach that tailors instruction to individual students' needs, pace, and learning styles. Making AI a critical enabler of truly responsive and differentiated teaching. Yet its effective implementation depends on teachers' readiness and self-regulated learning (SRL). This qualitative case study explored how SRL shapes elementary school teachers' readiness for AI-supported PAL and how they regulate planning, monitoring, and reflection during technology integration. Data were collected through semi-structured interviews and classroom observations involving five teachers and analyzed using thematic analysis guided by Zimmerman's SRL theory. Findings revealed three dimensions of readiness: goal-oriented technology planning, adaptive instructional adjustment, and critical engagement with AI outputs. Teachers' practices reflected the forethought, performance, and self-reflection phases of SRL. The study suggests that readiness for AI-supported PAL is fundamentally mediated by metacognitive, motivational, and behavioral self-regulation processes.