AI INTEGRATION IN TEACHING AND LEARNING: FACULTY TRAINING FOR THE USE OF ARTIFICIAL INTELLIGENCE IN HIGHER EDUCATION IN VIETNAM

By Le Ho Van Phang, Luu Thao Ly, Giang Gia Bao, Vu Hoang Long, Nguyen Tam Nhu Ngoc
DOI: 10.37550/tdmu.EJS/2025.04.687

This study examines the relationships between Training Quality (TQ), Institutional Support (IS), Faculty Readiness (FR), and Perceived Effectiveness (PE) in the context of artificial intelligence (AI) integration in higher education institutions (HEIs) in Vietnam. Employing Partial Least Squares Structural Equation Modeling (PLS-SEM), data from 418 faculty members from higher education institutions (HEIs) in Vietnam were analyzed to identify key factors influencing the adoption of AI in teaching. The findings reveal that both TQ and IS significantly enhance FR, underscoring the critical importance of comprehensive training programs and institutional resources for preparing faculty to adopt AI. Furthermore, FR has a substantial impact on PE and serves as a mediator between TQ and PE, as well as IS and PE. This highlights the pivotal role of faculty readiness in transforming training and support into perceived improvements in teaching effectiveness. The model demonstrates high predictive relevance for both FR (Q² = 0.55) and PE (Q² = 0.60), suggesting the robustness of the theoretical framework. Despite the study’s limitations, including its focus on Vietnamese HEIs and cross-sectional design, it provides valuable insights for designing effective faculty development and institutional support strategies to facilitate AI integration