Teaching Lab will develop an open source benchmark dataset made up of images of handwritten student math work, with the goal of improving the ability of multi-modal LLMs to understand and interact with handwritten student work—making it easy to measure the performance of leading models on highly relevant student math work. This project is a collaboration between Teaching Lab, ASSISTments and the Allen Institute for AI.
Ryan Knight
Head of Data & AI Practice, Insource Services, Inc.
Ryan Knight
Head of Data & AI Practice, Insource Services, Inc.
Ryan Knight leads the Data & AI Practice at Insource Services, Inc, where he helps mission-driven organizations develop custom AI-driven software. Previously, Ryan founded EdLight, PBC , an education technology startup that helps teachers see and respond to student thinking.
Alice Ng
Consultant, Teaching Lab
Alice Ng
Consultant, Teaching Lab
Alice is a subject matter expert at Teaching Lab with seven years of experience teaching math from 5th grade through Algebra 1 in New York City. She currently serves as the Director of Upper School Math for grades 5-12 at Ascend Public Charter Schools.
Kyle Lo
Lead Scientist, Allen Institute for AI
Kyle Lo
Lead Scientist, Allen Institute for AI
Kyle is a Lead Scientist at the Allen Institute for AI. Kyle specializes in natural language processing, machine learning and human-AI interaction. Recent projects include the Semantic Scholar project and OLMo.
Lucy Li
PhD Candidate, University of California, Berkeley
Lucy Li
PhD Candidate, University of California, Berkeley
Lucy is a PhD student at the University of California, Berkeley, affiliated with Berkeley Artificial Intelligence Research and the School of Information. Lucy’s research intersects natural language processing with computational sociolinguistics and cultural analytics. A significant segment of Lucy’s work emphasizes equity and fairness in AI.
Sami Baral
Phd Candidate, Worcester Polytechnic Institute
Sami Baral
Phd Candidate, Worcester Polytechnic Institute
Sami works in the ASSISTments lab at WPI, involving various projects to build better support for teachers to assess students’ work. Sami’s research revolves around organizing data of student responses to open-ended questions and building machine learning models utilizing natural language processing techniques to automatically assess and suggest feedback messages to teachers to give to their students. More recently, Sami has been analyzing and assessing students’ handwritten math work as images, applying computer vision and image processing methods.