Scaling Expertise via Language Models: With Applications to Education

Access to expertise shapes how individuals learn, develop, and succeed across society. For example, in education, experienced teachers teach students and train novice educators through effective interactions. However, access to expertise is limited, undermining learning at scale. While language models promise to democratize access, they often mimic surface-level patterns and lack the human touch needed to support learners through challenges. In this talk, I will present novel computational methods and interventions that embed expert-like thinking into language models and empower human novices in real-time interactions. First, I will present Bridge, an adaptation method that extracts expert reasoning from verbalized talk-aloud protocols to adapt language models for complex interactions. Then, I will introduce Tutor CoPilot, a novel Human-AI approach that provides expert-like guidance to tutors in real time. In the first randomized controlled trial of a Human-AI system for live tutoring, Tutor CoPilot significantly improves the quality of interactions for 900 tutors and 1,800 K-12 students from underserved communities.

Speaker: Rose Wang, Stanford University