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 latent expert reasoning 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 learning interactions for 900 tutors and 1,800 K-12 students from underserved communities.
Bio: Rose E. Wang is a Computer Science PhD candidate at Stanford University. She develops algorithms, benchmarks and large-scale interventions to tackle challenges in real-world interactions, with a focus on Education. Her work is deployed in industry and directly improves the education of under-served students through partnerships she has cultivated during her Ph.D., including Title I school districts and several education companies, impacting 200,000+ students, 1,700+ teachers, 16,100+ tutors, in millions of tutoring sessions across the U.S., UK and India. Her work is recognized by the 2025 Economic Report of the President, NSF Graduate Research Fellowship, CogSci Best Paper Award, NeurIPS Cooperative AI Best Paper Award, ICLR Oral, Rising Star in Data Science, Building Educational Applications Ambassador Paper Award, and the Learning Engineering Tools Competition Award.
To request accommodations for a disability please contact Emily Lawrence, emilyl@cs.princeton.edu, at least one week prior to the event.