Artificial Emotional Intelligence (AEI) represents a groundbreaking advancement in human-computer interaction, enabling systems to understand, interpret, and respond to human emotions. When integrated with wearable technologies, AEI offers unprecedented opportunities to monitor and support emotional well-being in educational settings. This work-in-progress (WiP) paper explores the intersection of AEI and wearable stress monitoring, focusing on its potential to enhance learning experiences, improve student well-being, and address emerging challenges in education.

Wearable devices, such as smartwatches and biosensors, provide real-time physiological data, including heart rate variability (HRV), electrodermal activity (EDA), and skin temperature, which are critical for stress detection. By combining these biomarkers with advanced AI models, AEI systems can deliver personalized, context-aware emotional support. For example, an AEI-powered virtual assistant could detect signs of stress in a student during an exam and suggest calming techniques or adaptive learning strategies.

The integration of large language models (LLMs) into AEI systems further enhances their ability to simulate empathy and provide emotionally intelligent responses. These systems can analyze qualitative data, such as journal entries or self-reported mood ratings, to offer tailored interventions. However, the deployment of AEI in education raises significant ethical concerns, including privacy, bias, and emotional dependency. This paper discusses strategies to address these challenges, such as privacy-preserving data processing, bias mitigation techniques, and frameworks for responsible AI use.

Applications of AEI and wearable stress monitoring in education are vast. In mental health, these systems can provide early detection of stress and anxiety, enabling timely interventions. In classroom settings, they can support personalized learning by adapting content delivery based on students’ emotional states. For educators, AEI offers tools to better understand and respond to student needs, fostering a more inclusive and supportive learning environment.

This work-in-progress paper also highlights future directions for research and development, including multi-modal AI systems that integrate wearables with voice, text, and environmental data, as well as longitudinal studies to assess the long-term impact of AEI on student well-being and academic performance. By addressing technical, ethical, and practical challenges, AEI and wearable stress monitoring have the potential to revolutionize education, creating emotionally intelligent systems that enhance both learning and well-being.

Keywords: Artificial Emotional Intelligence (AEI), wearable stress monitoring, emotion-aware systems, personalized learning, student well-being, ethical AI, large language models (LLMs), education technology, mental health in education.