A Novel Theoretical Framework for Culturally Adaptive Conversational AI in Nigerian Indigenous Language Contexts

Conversational AI systems designed for Nigerian indigenous language speakers must navigate not only linguistic but deep cultural adaptation requirements, including non-linear discourse conventions, oral tradition rhetorical forms, communal identity expression patterns, and culturally specific pragmatic norms that standard dialogue system design frameworks do not model. This study develops a novel theoretical framework for culturally adaptive conversational AI in Nigerian indigenous language contexts, using Yoruba, Igbo, and Hausa as formative cases. The framework, designated the Cultural Dialogue Adaptation Model (CuDAM), introduces three original theoretical components: a cultural pragmatics module that models language-context-specific speech act conventions, politeness strategies, and implicit meaning inference rules for each target language; a dynamic cultural context representation that tracks active cultural norms, relationship roles, and community identity markers across the conversation, adjusting response generation accordingly; and a cultural coherence evaluation metric assessing how well conversational AI responses conform to native speaker pragmatic expectations, operationalised through annotation studies with 150 native speakers per target language. CuDAM was implemented as a dialogue policy layer over a multilingual language model base and evaluated through task-oriented dialogues in three application domains (health advisory, agricultural guidance, government services). Native speaker cultural coherence ratings for CuDAM-enhanced responses were 34 percent higher than baseline multilingual model responses. The framework provides an original theoretical foundation for culturally grounded conversational AI design for African language communities.

Keywords: conversational AI, cultural adaptation, Nigerian indigenous languages, dialogue systems, pragmatics