以Ubuntu为指导的认知行为大型语言模型框架用于认知行为心理健康对话
An Ubuntu-Guided Large Language Model Framework for Cognitive Behavioral Mental Health Dialogue
AI总结：
本文提出一种结合Ubuntu哲学与认知行为疗法的AI心理健康对话系统，旨在提升非洲语境下的文化敏感性和治疗效果。
摘要：
南非日益加剧的心理健康危机，再加上对文化敏感护理访问有限，要求创新且基于情境的干预措施。尽管大型语言模型在心理健康支持方面显示出巨大潜力，但其主要以西方为中心的训练数据限制了非洲语境中的文化和语言适用性。本研究介绍了一个概念验证框架，该框架整合认知行为疗法与非洲哲学中的Ubuntu理念，以创建文化敏感、情感智能的AI驱动心理健康对话系统。该框架基于设计科学研究方法，应用了深入的理论和治疗改编以及表面的语言和交际文化改编。关键的认知行为疗法技术，包括行为激活和认知重构，通过Ubuntu原则重新解释，这些原则强调社区福祉、精神根基和相互联系。通过迭代的语言简化、精神上下文化和Ubuntu基础重构过程开发了文化适应数据集。经过微调的模型通过专家指导的案例研究进行评估，使用UniEval进行对话质量评估，同时采用额外的CBT可靠性和文化语言对齐措施。结果表明，该模型能够有效地进行富有同理心、情境感知的对话，符合治疗和文化目标。尽管尚未进行实时终端用户测试，但该模型已通过领域专家临床心理学家的严格审查和监督。研究结果突显了文化嵌入的情感智能在增强非洲语境中AI驱动心理健康干预的上下文相关性、包容性和有效性方面的潜力。
英文摘要：
South Africa's escalating mental health crisis, compounded by limited access to culturally responsive care, calls for innovative and contextually grounded interventions. While large language models show considerable promise for mental health support, their predominantly Western-centric training data limit cultural and linguistic applicability in African contexts. This study introduces a proof-of-concept framework that integrates cognitive behavioral therapy with the African philosophy of Ubuntu to create a culturally sensitive, emotionally intelligent, AI-driven mental health dialogue system. Guided by a design science research methodology, the framework applies both deep theoretical and therapeutic adaptations as well as surface-level linguistic and communicative cultural adaptations. Key CBT techniques, including behavioral activation and cognitive restructuring, were reinterpreted through Ubuntu principles that emphasize communal well-being, spiritual grounding, and interconnectedness. A culturally adapted dataset was developed through iterative processes of language simplification, spiritual contextualization, and Ubuntu-based reframing. The fine-tuned model was evaluated through expert-informed case studies, employing UniEval for conversational quality assessment alongside additional measures of CBT reliability and cultural linguistic alignment. Results demonstrate that the model effectively engages in empathetic, context-aware dialogue aligned with both therapeutic and cultural objectives. Although real-time end-user testing has not yet been conducted, the model underwent rigorous review and supervision by domain specialist clinical psychologists. The findings highlight the potential of culturally embedded emotional intelligence to enhance the contextual relevance, inclusivity, and effectiveness of AI-driven mental health interventions across African settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
