JMIR Publications

ABSTRACT

Abstract: This article proposes a medical education assisted teaching and student evaluation method based on facial expression recognition technology to address several challenges in traditional medical education. By introducing advanced facial expression recognition technology, students' emotional changes during the learning process can be captured and analyzed in real time, providing more comprehensive and detailed teaching feedback. This article elaborates on the four key steps of this method: data collection, facial expression recognition, result analysis, and teaching feedback, and explores its potential advantages in improving teaching effectiveness, optimizing personalized learning, and promoting teacher-student interaction. In the data collection process, students' facial expressions are recorded through multi angle high-definition cameras to ensure the comprehensiveness and accuracy of the data. The facial expression recognition process utilizes computer vision and deep learning algorithms to accurately identify students' emotional states. The result analysis stage organizes and statistically analyzes the identified emotional data, providing teachers with comprehensive feedback on students' learning status. In the teaching feedback stage, teaching strategies are adjusted based on the analysis results to improve teaching effectiveness. Although this method faces challenges such as technical accuracy, device dependency, and privacy protection, its application prospects in medical education are broad, and it is expected to significantly improve teaching quality and student learning experience. Keywords: medical education; facial expression recognition; auxiliary teaching; student assessment; teaching Innovation