The deep integration of artificial intelligence into education has made generative AI (GenAI) a promising instructional tool for supporting foreign language learners’ cross-cultural and linguistic expression. Despite its growing pedagogical popularity, few empirical studies have systematically examined English majors’ behavioral intentions to adopt GenAI for Chinese cultural expression in English. Grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT), this study extends the original framework by incorporating information quality and system quality to better suit culture-oriented foreign language learning contexts. Based on 521 valid responses, this study employed PLS-SEM to investigate the key determinants of students’ intention to use GenAI for enhancing their English-mediated representation of Chinese culture. The results demonstrate that information quality, system quality, performance expectancy, and facilitating conditions positively and significantly shape students’ behavioral intention. Unexpectedly, effort expectancy exerts a significant negative influence on usage intention. Mediation analyses further reveal that performance expectancy partially mediates the relationship between information quality and behavioral intention, whereas effort expectancy partially mediates the link between system quality and behavioral intention. The proposed model explains 71.1% of the variance in learners’ intention to use GenAI, indicating strong explanatory power. This study extends the theoretical applicability of UTAUT to AI-supported cross-cultural learning and culture-focused foreign language instruction. It also provides practical implications for the design, optimization, and pedagogical implementation of intelligent language learning tools in digital cultural teaching reform. 

Yining Zheng, Ran Huang, Shuhan Yang. Factors influencing undergraduate English majors’ behavioral intention to use GenAI for Chinese cultural expression in EFL: An extended UTAUT framework. Frontiers in Educational Research (2026), Vol. 9, Issue 9: 95-106. https://doi.org/10.25236/FER.2026.090914.

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