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Ngahane Nana, Liliane G.; Arslan, Amaniyel; Adamczyk, Sascha; Ciftcioglu, Nedim; Özmen, Senem

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Global engineering organisations are increasingly relying on guideline-based training to ensure modelling consistency, process standardisation, and effective knowledge transfer during CAD/PLM transitions. However, when such programmes are deployed across culturally diverse teams, differences in cognitive style, such as analytical versus holistic reasoning and rule-oriented versus context-oriented problem solving, can strongly influence how learners interpret guidelines, approach modelling tasks, and internalise new digital workflows. This article presents the conclusions drawn from a project conducted by Artech Consulting with an international organisation operating within the agriculture and pharmaceutical sectors. The project involved implementing guideline-based learning within engineering teams worldwide. Using qualitative observations, training evaluations and interviews with practitioners, the study examines how thinking patterns shaped by culture affect the uptake, use and perceived clarity of modelling guidelines during large-scale software transitions. The findings demonstrate that cultural cognition influences learning strategies and expectations regarding structure, autonomy, and the level of detail required in design instructions. While some teams benefit from highly prescriptive, step-by-step guidelines, others prefer examples, visual explanations or contextual framing. Misalignments in these preferences can lead to inconsistent modelling behaviours, misconceptions, and reduced confidence in newly introduced workflows. Building on these insights, the paper proposes a practical framework for tailoring guideline-based engineering training to culturally diverse audiences. This framework outlines design principles for balancing prescription and flexibility, integrating culturally resonant examples and supporting a shared understanding among distributed engineering teams. This demonstrates how cultural considerations can enhance the effectiveness of training, reduce ambiguity, and strengthen organisational learning during complex CAD/PLM transformations.

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