Culturally-Aware Prompting in conversational AI: supporting perceived communicative effectiveness in cross-cultural teams in Australia

With multilingual collaboration increasingly important in digital work environments, linguistic correctness is not sufficient any more for equitable and sustainable cross-cultural communication. This study empirically examines whether Culturally-Aware Prompting (CAP) can enhance the cultural appropriateness, pragmatic politeness, and collaborative effectiveness of conversational AI. In this study, CAP is defined as a structured prompt design approach that explicitly embeds cultural context into the instruction given to a language model. This study was conducted within Australia's culturally and linguistically diverse context. With a fully crossed experiment employing GPT-4o across three language pairs and four task types, finally generating 72 outputs, which were then evaluated through blind human ratings, automated politeness and semantic metrics, and statistical modelling. Results show that CAP significantly improves cultural appropriateness and pragmatic politeness without compromising semantic accuracy. Interaction analyses indicate that CAP produces stronger perceived benefits in language pairs and tasks with higher pragmatic sensitivity. Automated metrics broadly align with human ratings, although they are treated as complementary indicators rather than substitutes for human judgement. Mediation modelling further reveals that CAP enhances collaborative outcomes primarily by strengthening cultural and pragmatic alignment. These findings position CAP as a practical and scalable mechanism for fostering trust, reducing pragmatic friction, and supporting sustainable collaboration in multicultural teams. An empirically validated framework proposed by this study also lays a foundation for culturally adaptive AI communication design.