As Large Language Models (LLMs) are increasingly integrated into mental health applications, the prevailing paradigm of cultural neutrality risks creating a technical debt that undermines model generalization and safety. Current systems/frameworks, while focusing on multilingual capabilities, often fail to account for the diverse social and cognitive norms of global populations.This vision paper explores the transition from culturally invariant models toward a research trajectory that operationalizes cultural dimensions such as power distance and individualism as fundamental architectural parameters. Rather than viewing cultural adaptation as a post-hoc fine-tuning task, we advocate for systems that internally encode cultural variability as a primary driver of the model’s reasoning process. We outline a roadmap for architecturally encoding cultural variability as a firstclass parameter in AI/ML pipelines, moving beyond surface-level translation toward systems where cultural dimensions fundamentally shape model reasoning.

Artificial intelligence systems are often presented as universal, yet their interaction paradigms remain predominantly text-first, limiting alignment with primarily oral languages and communicative practices. Using Guaraní, an official and widely spoken language of Paraguay, as a motivating case, this work examines how language support risks remaining symbolic when spoken interaction is reduced to a speech-to-text interface. We explore an oral-first, multi-agent framing in which turn-taking, repair, shared context, and governance are treated as core components of interaction rather than peripheral features. By separating language understanding from the conversation state and permission mechanisms, the architecture makes conversational structure and control explicit, enabling reasoning over interaction dynamics rather than isolated commands. Framing conversational coordination as a cognitively motivated reasoning problem over shared state connects insights from human dialogue to the design of AI systems that are more interpretable and responsive in oral and low-resource settings.

We introduce FLAME (FLemish Accounts of Momentary Experiences), a corpus of nearly 25,000 personal narratives in Belgian-Dutch (Flemish), collected through experience sampling to support Natural Language Processing (NLP) research on an underrepresented variety. Such everyday narratives are rich in culturally grounded themes, but their informal register and low-resource setting make thematic extraction hard. Comparing K-Means, LDA, and BERTopic, we find that human evaluation favors BERTopic, which produces the most coherent, culturally resonant topics. FLAME, thereby, offers a new resource for studying everyday language use in a low-resource variety.

Mental health communication in India is linguistically fragmented, culturally diverse, and often underrepresented in clinical NLP. Current health ontologies and mental health resources are dominated by English or Western-centric diagnostic frameworks, leaving a gap in representing patient distress expressions in Indian languages. We propose the Cross-Lingual Graphs of Patient Distress Expressions (CL-PDE), a framework for building cross-lingual mental health ontologies through graph-based methods that capture culturally embedded expressions of distress, align them across languages, and link them with clinical terminology. Our approach addresses critical gaps in healthcare communication by grounding AI systems in culturally valid representations, enabling more inclusive and patient-centric NLP tools for mental health care in multilingual contexts.