ENHANCING CLASSROOM ENGAGEMENT THROUGH AI-POWERED EMOTIONAL, HEAD POSE, AND GAZE TRACKING: A NOVEL APPROACH TO RESPONSIVE TEACHING

Abstract

Active participation of students in classroom is crucial for enhancing the learning process. Their emotional state significantly influences not only the content they grasp but also their level of engagement during lessons and their overall academic performance. Emotions impact how motivated students are to study, concentrate, and manage their learning. Monitoring students’ emotions in the classroom and handling them properly are important for a better learning experience. However, it can be an added challenge for teachers who also need to focus on creating and teaching high-quality lessons. To support responsive teaching, we have developed an AI powered classroom monitoring tool that detects emotions and headpose, and tracks students’ eye movement so that the teachers can monitor students' emotional states and engagement levels. In this research, we address one of the limitations in the existing work, head-pose estimation to improve the model accuracy. This model includes the following steps: (1) analyzes students’ emotional states — such as confusion, happiness, and more — during the lesson, (2) tracks their gaze direction to determine if their focus is on the instructor, to their sides, or if their eyes are shut completely, and (3) monitors head orientation to identify where students spend most of their time looking. After completing the analysis over a specified span of time, the AI powered tool generates a detailed report on student focus and emotional status to present educators with statistics that can be used to tailor their teaching strategies whether it's online or in a classroom setting. As a result, teacher can improve the teaching materials for better content delivery and support adaptive teaching methods.