Artificial intelligence-assisted psychological intervention mechanisms for university students in the context of new media technologies: an analysis based on data from the national institute of mental health

Introduction With the pervasive influence of new media, mental health issues among college students have become increasingly prominent, manifested in high rates of emotional disorders, delayed interventions, uneven resource allocation, and limited attention to individual differences. This study aims to assess the mental health status of college students and explore the potential of artificial intelligence (AI) technologies in supporting psychological interventions. Methods Publicly available data from the National Institute of Mental Health (NIMH) were analyzed using descriptive statistical methods. Key variables included depression, anxiety, social support, quality of life, and technology usage. Based on these findings, a personalized AI intervention framework was developed, integrating psychological assessments, social support levels, and technology usage patterns. Results The analysis revealed that the average depression score among the student population was 7.5 ( SD = 4.2), and the average anxiety score was 6.8 ( SD = 3.9), highlighting the widespread prevalence of emotional issues. A significant correlation was also identified between technology usage frequency and negative psychological indicators. Discussion The study demonstrates the novelty of applying AI models to psychological interventions by leveraging intelligent perception, real-time feedback, and dynamic adjustment mechanisms. This personalized and data-driven approach enhances the efficiency and precision of mental health support. These findings provide both a theoretical foundation and a practical pathway for developing innovative mental health support systems in higher education.