Paper Number
ECIS2026-2081
Paper Type
SP
Abstract
Artificial Intelligence (AI), and Large Language Models (LLM) in particular, can advance global learning by enabling cross-cultural collaboration through translation, inclusive participation, enhanced communication, and feedback. Yet benefits vary with language proficiency and cultural biases in LLM design and outputs. Non-native speakers of English often face higher cognitive load and lower output quality in low-resource languages, while AI translation tools often miss cultural nuances and reflect U.S.-centred norms. In a survey of 502 students worldwide, language proficiency and nationality predicted LLM use frequency. Our main study will test how English language proficiency and cultural background influence LLM use frequency, perceived effectiveness of LLM, and perceived learning outcomes in international contexts.
Recommended Citation
Fleischmann, Carolin and Graupner, Emma, "Lost In Translation? Exploring Llm Use Across Language Proficiency And Cultural Boundaries" (2026). ECIS 2026 Proceedings. 9.
https://aisel.aisnet.org/ecis2026/comp_mgmt/comp_mgmt/9
Lost In Translation? Exploring Llm Use Across Language Proficiency And Cultural Boundaries
Artificial Intelligence (AI), and Large Language Models (LLM) in particular, can advance global learning by enabling cross-cultural collaboration through translation, inclusive participation, enhanced communication, and feedback. Yet benefits vary with language proficiency and cultural biases in LLM design and outputs. Non-native speakers of English often face higher cognitive load and lower output quality in low-resource languages, while AI translation tools often miss cultural nuances and reflect U.S.-centred norms. In a survey of 502 students worldwide, language proficiency and nationality predicted LLM use frequency. Our main study will test how English language proficiency and cultural background influence LLM use frequency, perceived effectiveness of LLM, and perceived learning outcomes in international contexts.