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TeachScope: UMKC professors use artificial intelligence to identify implicit biases

A team of UMKC faculty is using artificial intelligence to revolutionize the way students and teachers communicate with each other in the classroom.

The project was inspired by a conversation between Computer Science Professor Yugyung Lee, Associate Professor of Communication Studies Ye Wang and Associate Research Professor Alexis Petri.

The team wanted to find out why elementary-aged children were struggling to learn adequate mathematics skills. They wondered why students at such an early age were feeling discouraged from learning basic multiplication, addition and subtraction skills.

While our first instinct may be to sympathize with these children, Wang explains why this might be doing more harm than good.

“Teacher-student interaction will influence student’s self-confidence in their ability,” said Wang. “It’s not simple compliments, but you have to tell them they are able to put the effort in and do it.”

The team then began to look at this same student-teacher interaction in a college setting. They discovered they were seeing far fewer female students and minorities, especially in the STEM field.

They questioned if this interaction could be one of the factors leading to this disparity. And if so, which aspect was discouraging students? After more research, they came to the answer: implicit bias.

Implicit bias refers to attitudes or stereotypes towards groups of people that we have without our conscious knowledge. Since we don’t have conscious knowledge of these thoughts, it makes it hard to measure or self-report. This created the need for a new kind of tool for people to recognize their own biases.

“When you use a telescope to look at the sky, there are stars and planets. Without a telescope, you can see stars, but you can’t see those planets,” said Wang. “Our implicit bias is the planets. We needed a tool to help us see those planets. That’s why we call it TeachScope.”

This tool will use artificial intelligence and a Deep Learning model to collect different kinds of behavior data. Camera equipment and a facial recognition technology system will analyze a student’s facial expressions, body gestures and conversations in the classroom. This system will provide analytics to teachers to help them address implicit biases in their teacher-student interactions.

In today’s increasingly diverse society, the need for this technology is greater than ever. It might be a while before we see this project hit the ground though. The team has submitted funding proposals this year, but is still waiting on the results.

“We don’t have the necessary funding to support this idea, but we will keep trying if we have the chance,” said Wang.

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