Testing an AI-Enhanced Coached-Tutor Professional Learning Model for Scaling High-Dosage Tutoring

We tested a multi-party, multi-level AI-enhanced coach-tutor model, where AI feedback on tutoring discourse provided to instructional coaches (level 1) leads to improved tutor discourse practices (level 2), resulting in student achievement growth (level 3). We tested this model in the context of ANON tutoring (anonymized), a large high-dosage tutoring (HDT) provider (37 coaches, 177 tutors, 2,966 students). Using latent growth curve modeling and multi-level mod- erated mediation techniques, we validated key mechanisms of our theory of change and found that growth in tutors discourse moves that promoted rigorous thinking and building on student ideas was associated with student achievement growth and mediated baseline- to-growth pathways. Further, the extent of engagement with the AI feedback amplified key pathways in the theory of change. Overall, these findings provide support for the effectiveness of AI-enhanced coaching tools that emphasize discourse as a lever for improving student outcomes at scale.