This systematic literature review explores the integration of artificial intelligence (AI) in educational settings to assess and manage emotions. The review aims to provide a comprehensive overview of the current state of research, focusing on advancements, challenges, and opportunities in AI-driven emotional assessment. The study involved a search across academic databases, resulting in the selection of 41 articles that met the inclusion criteria. These articles were analyzed to extract key insights related to the integration of AI and emotional assessment in educational environments. The findings reveal a variety of AI-driven approaches used to capture and analyze students' emotional states during learning activities. The research is summarized into four fundamental topics: emotion recognition in education, technology integration and learning outcomes, special education and assistive technology, and affective computing. Key AI techniques employed include machine learning and facial recognition, which are used to assess emotions. These approaches demonstrate promising potential in enhancing pedagogical strategies and creating adaptive learning environments that cater to individual emotional needs. The review also identified emerging factors that require further investigation, such as federated learning, convolutional neural networks (CNN), recurrent neural networks (RNN), facial expression databases, and ethics in the development of intelligent systems. Despite the advancements, challenges related to accuracy, privacy, and cross-cultural validity were also identified. The synthesis of existing research highlights the need for further research into refining AI models for emotion recognition and emphasizes the importance of ethical considerations in implementing AI technologies within educational contexts. Overall, the review underscores the significance of AI in revolutionizing educational practices through emotion assessment. While advancements are evident, ongoing challenges must be addressed to fully realize the potential of AI in enhancing student learning and well-being.This systematic literature review explores the integration of artificial intelligence (AI) in educational settings to assess and manage emotions. The review aims to provide a comprehensive overview of the current state of research, focusing on advancements, challenges, and opportunities in AI-driven emotional assessment. The study involved a search across academic databases, resulting in the selection of 41 articles that met the inclusion criteria. These articles were analyzed to extract key insights related to the integration of AI and emotional assessment in educational environments. The findings reveal a variety of AI-driven approaches used to capture and analyze students' emotional states during learning activities. The research is summarized into four fundamental topics: emotion recognition in education, technology integration and learning outcomes, special education and assistive technology, and affective computing. Key AI techniques employed include machine learning and facial recognition, which are used to assess emotions. These approaches demonstrate promising potential in enhancing pedagogical strategies and creating adaptive learning environments that cater to individual emotional needs. The review also identified emerging factors that require further investigation, such as federated learning, convolutional neural networks (CNN), recurrent neural networks (RNN), facial expression databases, and ethics in the development of intelligent systems. Despite the advancements, challenges related to accuracy, privacy, and cross-cultural validity were also identified. The synthesis of existing research highlights the need for further research into refining AI models for emotion recognition and emphasizes the importance of ethical considerations in implementing AI technologies within educational contexts. Overall, the review underscores the significance of AI in revolutionizing educational practices through emotion assessment. While advancements are evident, ongoing challenges must be addressed to fully realize the potential of AI in enhancing student learning and well-being.