Intel and Classroom Technologies are creating tools to identify the emotions of students in virtual classrooms.

Intel and Classroom Technologies are working on tools that use artificial intelligence (AI) to detect the mood of children in virtual classrooms. The feature could be used to tell a teacher if a student was bored, confused, or distracted.

The Intel-developed software solution aims to apply the power of artificial intelligence to the faces and body language of digital students. According to Protocol, the solution is being distributed as part of the “Class” software product and aims to aid in teachers’ education techniques by allowing them to see the AI-inferred mental states (such as boredom, distraction, or confusion) of each student.

Intel aims to expand the program into broader markets eventually. However, the technology has been met with controversy.

Also read: Intel acquires AI specialist Granulate Cloud Solutions.

Giving the teacher insights to better communicate

The AI-based feature can be used to classify students’ body language and facial expressions whenever digital classes are held through the videoconferencing application. Citing teachers’ own experiences following remote lessons taken during the COVID-19 pandemic, Michael Chasen, co-founder and CEO of Classroom Technologies, told Tom’s Hardware he hopes its software gives teachers additional insights, ultimately bettering remote learning experiences.

“We can give the teacher additional insights to allow them to better communicate,” said Chasen, who said teachers have had trouble engaging with students in virtual classroom environments throughout the pandemic.

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.

However, 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.