Intel Calls Its AI That Detects Student Emotions a Teaching Tool. Others Call It ‘Morally Reprehensible’

When college instructor Angela Dancey wants to decipher whether her first-year English students comprehend what she’s trying to get across in class, their facial expressions and body language don’t reveal much. “Even in an in-person class, students can be difficult to read. Typically, undergraduates don’t communicate much through their faces, especially a lack of understanding,” said Dancey, a senior lecturer at the University of Illinois Chicago. Dancey uses tried-and-true methods such as asking students to identify their “muddiest point” — a concept or idea she said students still struggle with — following a lecture or discussion. “I ask them to write it down, share it and we address it as a class for everyone’s benefit,” she said. But Intel and Classroom Technologies, which sells virtual school software called Class, think there might be a better way. The companies have partnered to integrate an AI-based technology developed by Intel with Class, which runs on top of Zoom. Intel claims its system can detect whether students are bored, distracted or confused by assessing their facial expressions and how they’re interacting with educational content.

“We can give the teacher additional insights to allow them to better communicate,” said Michael Chasen, co-founder and CEO of Classroom Technologies, who said teachers have had trouble engaging with students in virtual classroom environments throughout the pandemic. His company plans to test Intel’s student engagement analytics technology, which captures images of students’ faces with a computer camera and computer vision technology and combines it with contextual information about what a student is working on at that moment to assess a student’s state of understanding. Intel hopes to transform the technology into a product it can distribute more broadly, said Sinem Aslan, a research scientist at Intel, who helped develop the technology. “We are trying to enable one-on-one tutoring at scale,” said Aslan, adding that the system is intended to help teachers recognize when students need help and to inform how they might alter educational materials based on how students interact with the educational content. “High levels of boredom will lead [students to] completely zone out of educational content,” said Aslan. But critics argue that it is not possible to accurately determine whether someone is feeling bored, confused, happy or sad based on their facial expressions or other external signals.