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Empowering Teenage Resilience: A Mobile App for Personalized Mental Health Support in the Post-covid Era

Authors

Guannan Du1 and Khoa Tran2, 1USA, 2California State Polytechnic University, USA

Abstract

In the wake of the Covid-19 pandemic, teenagers worldwide have faced unprecedented stress and mental health challenges. This research paper presents a novel mobile application, designed as a technologically advanced solution to support the mental well-being of this vulnerable demographic [11]. Leveraging cutting-edge AI technology, the app offers personalized advice and support based on individual user inputs, such as emotional states and preferences captured through diary entries [12]. It uniquely integrates location-based volunteer activity suggestions, aiming to engage teenagers in community service, thereby enhancing their sense of purpose and connection. The core of the application includes a sophisticated AI feedback system, personalized volunteer opportunities, and a secure personal journaling feature, all tailored to meet the diverse needs of teenage users. Experimental results have demonstrated the AI system's superior accuracy in providing advice, surpassing that of human volunteers, with an accuracy rate of 80% compared to the volunteers' 75%. Additionally, user engagement experiments using A/B testing methods on UI design changes showed a significant increase in user interaction and time spent within the app, highlighting the effectiveness of the enhanced card layout over traditional Gridview layouts. These findings underscore the application's potential not only in delivering accurate, personalized mental health support but also in fostering a greater sense of community and engagement among teenagers. By addressing the pressing need for accessible, personalized mental health solutions, this work contributes significantly to the discourse on leveraging technology to mitigate the mental health crisis among youth, advocating for the broader adoption and continuous development of such innovative approaches.

Keywords
Mobile Application, Interpret, Artificial Intelligence, Machine Learning