Abstract
This study examines how peer-group composition shapes human capital accumulation during the adoption of artificial intelligence technologies. We implement a randomized field experiment within a large-scale AI teacher training program involving 750 in-service teachers.
Participants are randomly assigned into groups of five teachers during collaborative learning activities embedded throughout the training program. The experiment varies whether teachers are assigned to peer groups composed of educators who share a common field of expertise (STEM, non-STEM, or primary education) or to mixed groups containing teachers from different educational domains.
The study investigates whether learning is more effective when peer interactions occur within a shared domain of expertise or across domains with heterogeneous knowledge bases. Within-domain groups may facilitate communication, coordination, and transfer of domain-specific tacit knowledge, while cross-domain groups may generate broader knowledge spillovers and exposure to diverse pedagogical approaches.
The intervention is integrated into collaborative AI-related activities, including lesson design, prompt engineering, peer evaluation, and assessment of AI-generated educational materials. The study measures human capital accumulation using assessments of AI competency, productivity, AI oversight capabilities, and the quality of AI-assisted educational outputs.
The primary outcomes include post-training AI knowledge, task performance, and subsequent adoption of AI tools in educational practice. Secondary outcomes examine collaboration patterns, participation, confidence in AI use, and heterogeneity in peer-learning effects across teacher backgrounds.
This project contributes to the literature on peer effects, human capital accumulation, and knowledge transfer by examining how shared expertise and cross-domain interactions shape learning during technological change.