Photo caption: Professor Vik Naidoo, Head of Department – Management, Marketing and Tourism, UC Business School
In a new paper, ‘Culturally responsive AI chatbots: From framework to field evidence’, Te Whare Wānanga o Waitaha | University of Canterbury (UC) Professor Vik Naidoo and co-author Karman Kaur Chadha argue that AI systems designed around largely Western assumptions often perform poorly in different cultural settings. The result can be more awkward interactions, which can lead to lower trust, weaker engagement, and systems that fail the people they are meant to serve.
Their paper introduces the Culturally Responsive AI (Chatbot) Framework, or CRAIF-C, a practical model for building AI-powered chatbots that are designed with cultural diversity in mind from the outset rather than treated as an afterthought.
CRAIF-C is a four-part framework for building culturally responsive chatbots across the full AI lifecycle. It combines Enculturation, which embeds cultural norms, language, and context into data and design; Adaptive Interaction, which adjusts tone, pacing, and style in real time; Explainability and Transparency, which provides culturally appropriate forms of explanation; and Governance and Accountability, which embeds oversight, cultural risk assessment, and community-informed review. Overall, the framework treats cultural fit as a core design requirement rather than an afterthought, shaping everything from training data and interaction design to explanation and governance so chatbots are more natural, trustworthy, and appropriate across different cultural contexts.
“People often think cultural awareness is translating words, but it’s much more than language. It’s about context, social norms, communication styles, and how people interpret the world around them,” Professor Naidoo says.
Many AI systems reflect Western logic because they are trained mainly on Western data. “If it is trained mainly on Western data, then the outputs it produces will also reflect Western assumptions,” he says.
That becomes a problem when chatbots are deployed globally, especially with firms trading across borders. A system may appear efficient but still fails if users do not relate to it or trust it.
While working with an AI development company in Sydney, Professor Naidoo helped train engineers to think about cross-cultural communication at the beginning of the design process, rather than simply translating English-language outputs at the end.
The company had been deploying chatbot systems in countries including Indonesia, Thailand and Vietnam, but was not getting the user engagement they had expected.
“What they were finding was that consumers simply weren’t engaging with the chatbots, so we started asking what cultural nuances needed to be built into the models from the start.”
Sometimes the differences were subtle. In a Western setting, a chatbot might open with small talk about the weather. Whereas in Jakarta, Professor Naidoo says, a question about traffic could feel more natural and relevant.
The paper argues these differences should be considered across the full AI lifecycle: from training data and interaction design to explainability, transparency and governance.
Professor Naidoo says the issue matters not only for international organisations, but also for culturally diverse countries such as New Zealand and Australia.
“At the moment, the conversation around AI is heavily focused on cost-cutting and efficiency, but from a marketing and end-user perspective, the real question is: are we creating value?” he says. “If people don’t trust the technology or can’t engage with it, then it hasn’t solved the problem.”
As AI becomes more deeply embedded in everyday services, he says understanding culture will be essential to making chatbots useful, trustworthy and effective. For now, he hopes the paper helps move the conversation beyond the hype surrounding AI and toward better design practice.
