ArticleFrontiers in psychology2026
Utilizing artificial intelligence to optimize psychological trauma intervention in social assistances.
Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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3 authors.
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Abstract
Introduction: Psychological trauma is prevalent among beneficiaries of social assistance, posing significant challenges to effective intervention. Traditional psychological support methods are often constrained by limited resources, delayed responses, and insufficient personalization. Against this backdrop, artificial intelligence (AI) offers new opportunities to enhance the efficiency and precision of psychological trauma interventions. This study explores the feasibility and effectiveness of integrating AI technologies into social assistance systems. Methods: This study adopts a mixed analytical approach. First, it reviews the current applications of AI in mental health, focusing on natural language processing, emotion recognition, and personalized recommendation systems. Based on this, an AI-driven framework for psychological trauma intervention is proposed, including the development of intelligent auxiliary diagnostic systems and personalized intervention plans. Empirical data are collected through questionnaire surveys to evaluate user experiences and intervention outcomes. Results: The findings indicate that AI-assisted interventions significantly improve user satisfaction and mental health outcomes. Specifically, 75% of respondents report being satisfied or very satisfied with the intelligent mental health system. Approximately 60% of participants experience noticeable improvements in their mental health status. Furthermore, 85% of respondents consider the personalized intervention plans to be well-aligned with their individual needs and circumstances. Discussion: The results demonstrate that AI technologies can effectively address key limitations of traditional psychological interventions by enhancing accessibility, responsiveness, and personalization. The integration of AI into social assistance systems not only improves intervention outcomes but also offers scalable solutions for resource-constrained settings. This study provides empirical support for the application of AI in social assistance and highlights its transformative potential in advancing mental health governance.
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