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生成式人工智能赋能社区心理健康教育的实践策略探索

Exploring Practical Strategies for AIGC in Empowering Community Mental Health Education

Psychology of China / 2025,7(4): 438-442 / 2025-04-16 135 31

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• Authors: 单钰玲      张俊权     
• Information:

  广西师范大学教育学部，桂林

• Keywords: 生成式人工智能；社区心理健康教育；个性化干预；实践策略
• AIGC; Community mental health education; Personalized interventions; Practical strategies
• Abstract: 近年来，随着生成式人工智能（AIGC）技术的迅速发展，其在心理健康教育领域的普及程度显著提升，且在社区心理健康教育服务领域的应用潜力日益显现。本文基于AIGC的技术特性，系统地探讨其在赋能社区心理健康教育方面的逻辑框架与实践策略，以进一步发挥AIGC的现实价值。AIGC可通过动态需求评估、个性化干预、科普教育等路径来提升社区心理健康服务的可及性、精准性与普惠性。同时，结合国内外现有文献，本文揭示了AIGC在与社区心理健康服务深度融合过程中所面临的多重挑战，例如隐私泄露风险、算法偏见、情感理解局限性以及社区接受度差异等，并在此基础上提出构建“人机协同”模式、强化数据匿名化与算法透明度、加强技术培训与社区科普宣传等应对策略，为社区心理健康教育工作者提供可操作的实践指导。此外，本文进一步展望了AIGC技术与社区心理健康教育服务的深度融合路径，提出未来研究需聚焦多模态情感计算技术研发、跨文化适应性算法优化以及差异化应用场景开发，推动AIGC与社区心理健康教育的深度整合，为构建智能化、普惠化的社会心理健康服务体系提供理论支撑与实践指引。
• 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.
• DOI: https://doi.org/10.35534/pc.0704069
• Cite: 单钰玲，张俊权．生成式人工智能赋能社区心理健康教育的实践策略探索［J］．中国心理学前沿，2025，7（4）：438-442．