Bridging theory and practice: assessing how pre-service teachers promote self-regulated learning with generative AI through lesson plan analysis

As education shifts toward student-centered learning, evaluating pre-service teachers' ability to foster self-regulated learning (SRL) is essential. This study examines lesson plans as performance-based assessment tools for evaluating pre-service teachers' competence in designing SRL-supportive instruction in online courses. Through qualitative analysis of 37 lesson plans created by 73 pre-service teachers, the research applies a three-dimensional analytical framework focusing on SRL strategy implementation, technology integration, and pedagogical competence. Findings reveal significant variability in how teachers structure SRL, scaffold autonomy development, and incorporate instructional technologies. High-quality lesson plans explicitly embedded metacognitive scaffolds and included structured support for student autonomy, while weaker plans provided inadequate SRL elements and lacked clear instructional rationales. Technology integration varied, with some lesson plans leveraging digital tools to enhance reflection and feedback, while others used technology in a passive, content-delivery manner. These findings reinforce the validity of lesson plans as assessment tools, offering teacher educators a structured alternative to self-reports in evaluating pre-service teachers' instructional reasoning and readiness to promote SRL in diverse learning environments.