Exploring Practical Strategies for AIGC in Empowering Community Mental Health Education

In recent years, the rapid advancement of AI-Generated Content (AIGC) technology has significantly increased its adoption in mental health education, with growing potential for application in community-based mental health education services. Building upon AIGC’s technical characteristics, this paper systematically investigates its logical framework and practical strategies for empowering community mental health education, thereby amplifying AIGC’s real-world value. AIGC enhances the accessibility, precision, and inclusivity of community mental health services through dynamic needs assessment, personalized interventions, and science-based education. By synthesizing existing domestic and international literature, this study identifies multifaceted challenges in AIGC’s deep integration with community mental health services, including privacy breach risks, algorithmic bias, limitations in emotional comprehension, and disparities in community acceptance. Corresponding countermeasures are proposed, such as establishing human-AI collaboration models, enhancing data anonymization and algorithm transparency, and implementing technical training with community science outreach programs, providing actionable guidance for mental health practitioners. Furthermore, this paper envisions future integration pathways between AIGC and community mental health education, advocating for research priorities in three domains: (1) Developing multimodal affective computing technologies. (2) Optimizing cross-cultural adaptive algorithms. (3) Exploring differentiated application scenarios. These directions aim to facilitate the profound convergence of AIGC with community mental health education, ultimately contributing theoretical foundations and practical blueprints for establishing intelligent, equitable social mental health service systems.