Culturally responsive AI chatbots: From framework to field evidence

As AI systems become part of everyday life around the world, their failure to recognise and respond to cultural differences can erode trust, reduce engagement, and undermine legitimacy. This paper introduces the Culturally Responsive Artificial Intelligence (Chatbot) Framework (CRAIF-C), a practical, modular approach to building AI chatbots that understand and respect cultural diversity. CRAIF-C is novel in that it operationalises cultural responsiveness across the entire AI lifecycle, combining domain-specific technical methods with validated measurement tools and multi-context empirical testing. It addresses persistent limitations of earlier approaches, such as Value-Sensitive Design or Participatory AI, which often remain conceptual, sector-bound, or late-stage interventions. CRAIF-C works across four key domains: Enculturation, Adaptive Interaction, Explainability & Transparency, and Governance & Accountability. The framework's effectiveness is demonstrated through four complementary studies, which consistently show that AI chatbot systems using CRAIF-C achieve meaningful gains in cultural fit, natural communication, clear explanations, user trust, and sustained engagement. By incorporating cultural sensitivity into the core of AI chatbot design, CRAIF-C provides a roadmap for creating technology that is both technically capable, socially aware, ethically robust, and globally adaptable.