Emotion recognition technology has become a prominent topic in artificial intelligence applications in education. These systems analyze students' facial expressions, voice patterns, and behavioral signals to identify emotional states such as engagement, boredom, or confusion during learning activities. Educational institutions and developers present these tools as means to improve teaching, personalize learning experiences, and track student engagement. However, their use raises significant ethical issues related to privacy, surveillance, data protection, and algorithmic bias ^{[1, 2]}.

This research examines the ethical implications of emotion recognition technology in educational settings by reviewing academic studies and technical reports published between 2019 and 2025. Sources include peer-reviewed journals, IEEE and ACM conference papers, and research databases including Google Scholar and arXiv. The analysis highlights concerns around biometric data collection and consent, cultural bias and accuracy limitations in emotion detection algorithms, and the psychological impact of continuous surveillance on students. The study concludes that while emotion recognition technologies offer potential benefits for engagement analysis, their deployment must be carefully regulated, ethically designed, and accompanied by transparent data practices, informed consent frameworks, and fair algorithmic standards ^{[3, 4, 5]}.