Emotions play a critical role in developing learning experiences and outcomes in educational environments. However, many teaching methods fail to address students' emotional responses due to constraints such as large class sizes and limited time. To enhance engagement and learning outcomes, emotion-recognition systems should be integrated with AI technologies. This research will highlight how AI-driven emotion-recognition chatbots are used as virtual teaching assistants, and explore their potential to improve educational environments by providing emotionally responsive interactions.

This research presents a systematic literature review of current state in emotion-detection technologies and their application in educational environments. The existing works was analyzed to identify benefits, limitations, and challenges associated with the deployment of emotion-sensitive AI tools. It is found that detecting and reflecting emotions like confusion, curiosity, and frustration, can foster a more interactive and supportive learning environment. Key challenges such as ethical concerns, data privacy, and algorithmic biases emphasize the complexity of implementing these technologies effectively.

Based on these results, we provided guidelines for designing emotion-aware chatbots that align with educational goals. These include strategies for accurate emotion detection, personalization of responses, and ensuring ethical and secure data handling practices.

By integrating emotional intelligence into AI tools, this research creates a paradigm shift in education, where emotionally aware chatbots serve as empathetic teaching assistants. Such integration promises to enhance student engagement, personalize learning experiences, and address individual challenges, which will contribute to improved academic performance and well-being.

Keywords: Emotion Recognition, Education, Chatbots.