HAT

Picture of three kids and a math tutor around a table. There's a laptop open and the tutor is holding a whiteboard with math-related terms.
Problem

Research has demonstrated the effectiveness of human high-dosage tutoring to improve student learning outcomes and close achievement gaps. 

However, a central challenge remains: How might we scale human tutoring while maintaining effectiveness and quality of service?

 

Opportunity

Our key insight is to blend what AI does best - finding patterns in large volumes of data - with what humans do best - nurturing and caring for others. 

As part of the HAT project, we are designing a hybrid human-AI solution to scale the effectiveness of human tutoring. 

 

Approach

Our team uses ASR and discourse classification models to analyze tutorial sessions for use of research-based practices. 

These practices are backed by the academically productive talk (APT) and belonging-centered instruction frameworks.