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The options for Artificial intelligence (AI) tools used in teacher education are increasing daily, but more is only sometimes better for teachers working in already complex classroom settings. This team discusses the increase of AI in schools and provides an example from administrators, teacher educators, and computer scientists of an AI virtual agent and the research to support student learning and teachers in classroom settings. The authors discuss the creation and potential of virtual characters in elementary classrooms, combined with biometrics and facial emotional recognition, which in this study has impacted student learning and offered support to the teacher. The researchers share the development of the AI agent, the lessons learned, the integration of biometrics and facial tracking, and how teachers use this emerging form of AI both in classroom-based center activities and to support students’ emotional regulation. The authors conclude by describing the application of this type of support in teacher preparation programs and a vision of the future of using AI agents in instruction.
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Computer Vision for Attendance and Emotion Analysis in School Settings
This paper presents facial detection and emotion analysis software developed for use in the classroom, even if you are a beginner or intermediate coder. The goal is to provide a tool that reduces the time teachers spend taking attendance while also collecting data that improves teaching practices. Disturbing current trends regarding school shootings motivated the inclusion of emotion recognition so that teachers are able to better monitor students’ emotional states over time. This will be accomplished by providing teachers with early warning notifications when a student significantly deviates in a negative way from their characteristic emotional profile. This project was designed to give students and teachers a hands- on project to implement in the classroom for the purpose of learning to use concepts from programming, computer vision, and machine learning. It is this team’s hope that the code presented will serve to save teachers time, help teachers better address student mental health needs, and motivate students and teachers to learn more computer science, computer vision, and machine learning as they use and modify the code in their own classrooms. Important takeaways from initial test results are that increasing training images increases the accuracy of the recognition software, and the farther away a face is from the camera, the higher the chances are that the face will be incorrectly recognized. The software tool is available for download at https://github.com/ferrabacus/Digital-Class.
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- Award ID(s):
- 1710716
- PAR ID:
- 10088294
- Date Published:
- Journal Name:
- IEEE 9th Annual Computing and Communication Workshop and Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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