An Ubuntu-guided large language model framework for cognitive behavioral mental health dialogue

Abstract

Introduction: 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 (LLMs) 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 Behavioural Therapy (CBT) with the African philosophy of Ubuntu to create a culturally sensitive, emotionally intelligent, AI-driven mental health dialogue system. Methods: Guided by a Design Science Research (DSR) methodology, the framework applies both deep (theoretical and therapeutic) and surface-level (linguistic and communicative) cultural adaptations. Key CBT techniques, Behavioural Activation and Cognitive Restructuring, were reinterpreted through Ubuntu principles emphasizing 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 and additional measures of CBT reliability and cultural-linguistic alignment. Results: 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. Discussion: The findings highlight the potential of culturally embedded emotional intelligence in enhancing the contextual relevance, inclusivity, and effectiveness of AI-driven mental health interventions across African settings.