From dialogue to differentiation: AI-agent-supported coaching for teacher efficacy with multilingual language learners

This study, published in Frontiers in Education, aimed to enhance the instructional expertise of 5th-grade teachers in integrating oral and written language development for multilingual language learners (MLLs) within Tier 1 differentiated instruction. It utilized a job-embedded professional development model focused on data-based decision-making (DBDM). The research explored coaches' perceptions of this model, teachers' views on AI agents for rubric design, teachers' efficacy with DBDM for MLLs, and implications for MLL English language proficiency.

This study is strategically important as it addresses the critical challenge of enhancing instructional effectiveness for multilingual language learners through professional development. It also explores the practical application of AI tools in curriculum development, indicating potential future directions for educational support and resource creation.

Coaches' perceptions of the professional development model's impact on teacher instructional efficacy with MLLs.

Forward consideration, not a verified fact.