UMD Researchers to Build AI Database to Improve Math Learning Outcomes

From left: Jing Liu (lead-PI, College of Education), Wei Ai (co-PI, College of Information and UMIACS), and Ph.D. students Meiyu Li (information studies), and Paiheng Xu (computer science) discuss the use of AI to advance math education for fourth through eighth graders.

Educators are looking at AI to help close this gap, whether deploying natural language processing software to scrutinize transcripts of classroom instruction, or employing machine learning to evaluate videos of how or when students take notes or raise their hands, or where teachers are positioned in the classroom while speaking.

But if the data used to train current AI models is of low quality or does not have the kind of rigorous math instruction educators would like to see, it won’t capture the rich multimodal nature of classroom instruction.

The potential uses of the new dataset are vast, said Liu. Researchers from various disciplines—from math educationists to psychologists to economists—could use it to explore topics like understanding students’ sense of belonging in classrooms and analyzing teaching quality.

Additionally, edtech companies could use the data to train their large language models for curriculum design, while AI developers could use it to train new models to improve computer vision techniques or speech recognition systems used in education.

Assisting Liu on the project are Wei Ai, an assistant professor in the College of Information; Heather Hill, a professor of teacher learning and practice at Harvard University; and Dora Demszky, an assistant professor of education data science at Stanford University.

For their project, data science experts on the team will add additional content to the classroom recordings, including student and teacher post-lesson surveys, lesson plans, classroom materials, administrative data and test scores. This will allow the recorded transcripts to include detailed annotations of key teaching moves and student mathematical practices, the researchers said.

For Liu and Ai, ensuring that the dataset is easily accessible is also crucial. The team wants the barrier to entry of using the data to be quite low to allow researchers from a broad array of scientific and educational backgrounds to use it, said Ai, who has a joint appointment in the University of Maryland Institute for Advanced Computer Studies (UMIACS).

The computational end of the project will be supported by UMIACS, with the Artificial Intelligence Interdisciplinary Institute at Maryland (AIM) aiding with several data sharing tasks. Additional help will come from the College of Education’s Center for Educational Data Science and Innovation, which Liu and Ai have set up to serve as a hub for research in AI and education.

The Gates Foundation/Walton Family Foundation-funded initiative is the latest in a series of collaborations between Liu and Ai in the field of education and AI. In 2023, they received a Grand Challenges Grant from UMD’s Division of Research to measure and improve equity in K–12 math classes with machine learning. Last year, they were awarded $1.5 million from the National Science Foundation to continue advancing this work with a focus on lesson planning. Around the same time, the team also received a seed grant from the Institute for Trustworthy AI in Law & Society (TRAILS) to address disparities in PK–12 education that are predictable by race and ZIP code.