Abstract:

In recent years, the integration of wearable technology with emotional intelligence coaching, powered by deep reinforcement learning and artificial intelligence, has emerged as a pioneering approach to enhance mental health support for graduate students. This research addresses the significant issues of stress and burnout prevalent in the graduate student community. The study utilizes advanced wearables, like the Empatica Embrace Plus, for continuous, real-time monitoring, combined with customized emotional intelligence coaching. This strategy aims to transform existing mental health support methods within academic settings, especially in engineering education, offering a dynamic and student-centered solution.

This work explores the potential of artificial intelligence, especially deep reinforcement learning techniques, to improve the efficacy and impact of wearable technology in mental health support for graduate students. Firstly, the study discusses the integration of wearable technology in monitoring various physiological signals, such as electrodermal activity and heart rate variability. Next, it presents applications of emotional intelligence coaching, informed by the insights derived from wearable data and Emotional Quotient Inventory 2.0 assessments. This approach facilitates personalized and effective interventions for enhancing students' emotional regulation skills and coping mechanisms for academic stressors. Furthermore, by highlighting successful case studies and initial findings of the research, it demonstrates correlations between physiological data from wearables and emotional assessments. These findings also reveal patterns of stress and well-being linked to demographic factors among the participants.

Artificial intelligence, particularly deep reinforcement learning, has shown significant promise in various fields, including mental health support and wearable technology. In recent years, researchers have begun exploring its potential in education, specifically for mental health support in engineering education. This study presents a comprehensive review of recent advancements in AI and reinforcement learning techniques applied to wearable technology for mental health support. The research explores how these techniques can analyze emotional states and stress patterns, and optimize interventions for student mental health support. The study also discusses the future directions and challenges in incorporating AI-enhanced wearable technology with emotional intelligence coaching in engineering education.

This research contributes to the growing body of knowledge on the potential of artificial intelligence, emotional intelligence, and wearable technology in mental health support for graduate students, particularly in engineering. The findings suggest that this integrative approach can provide more dynamic, personalized, and effective mental health support, enhancing the overall well-being and academic performance of engineering students.

Keywords:

Mental health, philosophy of engineering education, data correlation, factor analysis, stress.