2026-27 Seminars and Recordings

January 1, 0001

September 255, 2026, 11 am CST

MetaMate: Automating Study Coding for Educational Systematic Reviews with Large Language Models

Speaker: Dr. Xue Wang, Johns Hopkins
Description: Data extraction is the slowest, most error-prone stage of a systematic review. MetaMate is a free, open-source web tool that uses large language models to extract study-level data from uploaded PDFs in seconds, using a customizable coding scheme and multimodal parsing of tables and figures. In evaluations against trained human coders on two datasets (32 and 113 studies), MetaMate matched or exceeded coder accuracy on most participant and intervention elements. This session demonstrates MetaMate live on real education studies, shares what we learned about where it succeeds and fails, and offers practical guidance for using AI extraction responsibly in your own reviews.
Video Recording
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