Generative AI Application Readiness Among Chinese University EFL Teachers: The Predictive Role of Organizational Support
DOI: 10.23977/aduhe.2026.080302 | Downloads: 3 | Views: 47
Author(s)
Jie Zhang 1,2
Affiliation(s)
1 School of English Language and Culture, Xi'an Fanyi University, Xi'an, Shaanxi, China
2 General Graduate School, Woosong University, Daejeon, South Korea
Corresponding Author
Jie ZhangABSTRACT
Against the backdrop of in-depth integration between artificial intelligence and higher education, generative artificial intelligence (GAI) has gradually been integrated into university classroom teaching and has become a crucial driver for the reform of college English teaching. As the core practitioners of AI teaching implementation, university EFL teachers' generative AI application readiness directly determines the practical effect of the intelligent transformation of teaching. Using university EFL teachers in Chinese universities as research subjects, this study explores teachers' overall generative AI application readiness, the current state of university organizational support for AI teaching integration, and the predictive effect of organizational support on teachers' AI application readiness. This study collects empirical data through questionnaire surveys and analyzes the data using reliability analysis, descriptive statistics, Pearson correlation analysis, and regression analysis. The results show that university EFL teachers in China have a moderately high level of generative AI application readiness, university organizational support for AI teaching integration is relatively insufficient, and organizational support can positively and significantly predict teachers' generative AI application readiness.
KEYWORDS
University Teachers; College English; Generative AI; Application Readiness; Organizational SupportCITE THIS PAPER
Jie Zhang. Generative AI Application Readiness Among Chinese University EFL Teachers: The Predictive Role of Organizational Support. Adult and Higher Education (2026). Vol. 8, No. 3, 10-20. DOI: http://dx.doi.org/10.23977/aduhe.2026.080302.
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