Literature review on large language models (LLMs) for cross-cultural project management

Abstract As large language models (LLMs) become increasingly embedded in global project environments, they are reshaping how people execute tasks, communicate, and collaborate. Although multilingual advances and established translation and interpreting practices can reduce linguistic barriers, semantic accuracy alone does not ensure culturally or pragmatically appropriate interaction in cross-cultural project teams. Misalignment may still arise from implicit norms, values, and context-sensitive expectations that current LLMs do not consistently represent. Using a scoping review methodology, this study synthesises 69 interdisciplinary studies and examines what is currently known about the capacity of culturally adaptive LLMs to support communication processes and collaboration outcomes in cross-cultural project management contexts. The findings are organised into four thematic domains: human–AI collaboration, cross-cultural project communication, LLM cultural and multilingual optimisation strategies, and challenges in evaluating cultural–pragmatic performance. The review indicates that LLMs can contribute to selected efficiency and coordination outcomes, but may also reproduce dominant cultural norms. Moreover, the reviewed technical studies predominantly use semantic or task-oriented metrics, which provide limited evidence about pragmatic alignment and relational effects. The article identifies research gaps in AI-mediated communication, cultural evaluation, and governance-oriented assessment and situates culturally adaptive LLMs within an established socio-technical debate about responsible and inclusive global collaboration.