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Abstract
This study investigates the therapeutic potential of using Artificial Intelligence Generated Music in addressing the impacts of societal pressures on mental well-being. After the 2020 COVID-19 epidemic, industrial and economic uncertainty increased dramatically, and the emotional and psychological state of the individual became a stressful phenomenon. The WHO recognizes that 70% to 90% of psychosomatic disorders are primarily due to emotional stress and psychological. Music can uplift the heart, beat depression, relieve pain, and promote physical and mental health. The "emotional" medicine of the mind is music. Thus, this study focuses on the issue of 1) addressing the psychological aspects of preventive care and reducing stress, anxiety, and potential physical risks, 2) using music as an emotionally connected tool to integrate psychological preventive care and mental health impacts, 3) enhancing overall health prevention, and to promote psychological well-being. This study employs a participatory framework. Individuals actively engage with AI algorithms to co-create personalized content tailored for pressure healing. The study utilizes a mixed-methods approach, combining quantitative assessments and text analysis techniques to evaluate the impact of participatory AI interventions comprehensively. Participants collaboratively contribute to the customization process, providing input and preferences that guide the generation of AI-generated content. The study also employs a structured process to ensure meaningful participant engagement, leveraging AI algorithms to analyze user feedback and preferences, facilitating a personalized content creation experience. The quantitative assessments involve text analysis techniques applied to user-generated content, utilizing natural language processing to extract sentiments, themes, and contextual insights related to pressure, well-being, and the effectiveness of pressure healing. This comprehensive analysis aims to capture quantitative metrics and the nuanced qualitative aspects embedded within participant narratives. Preliminary findings from the pilot study indicate a positive impact of participatory AI-generated music on pressure healing. Quantitative data reveal trends suggesting reductions in self-reported pressure levels, while qualitative insights provide a nuanced understanding of participants' subjective experiences and perceptions. These initial results underscore the potential efficacy of the intervention. Future research should refine text analysis methodologies to enhance sentiment extraction accuracy and thematic identification. Expanding the study across diverse populations and cultural contexts will contribute to the generalizability of the intervention. Longitudinal studies are recommended to explore the sustained effects of participatory AI interventions over time. Incorporating real-time physiological feedback mechanisms and exploring ethical considerations associated with AI in mental health interventions constitute critical areas for future exploration. Additionally, integrating participatory AI within broader mental health frameworks and treatment plans offers promising avenues for advancing the field.
Keywords: Artificial Intelligence Generated Music, Pressure Healing, Participatory Framework, Text Analysis, Quantitative and Qualitative insights
