AI-driven governance of student status and academic credentials in higher education and capacity building for public health education management

Abstrakt

Introduction This study examines the association between AI-enabled university governance and public health education capacity development and the indirect roles of professional development and technology integration. Four hypotheses were tested: AI-enabled university governance is positively associated with public health education capacity development (H1); professional development (H2) and technology integration (H3) represent specific indirect components; and professional development followed by technology integration represents a sequential indirect association (H4). Methods A cross-sectional survey was conducted with 301 university educators from China, the USA, Germany, India, and Brazil who participated in university-wide health education activities. The four constructs were assessed using five-point Likert-type measures, with Cronbach's alpha values ranging from 0.78 to 0.91. Bayesian structural equation modeling was used to evaluate the hypotheses. Results and Discussion The direct association between AI-enabled university governance and public health education capacity development had a posterior mean of 0.31 (95% CrI [0.17, 0.45]), supporting H1. The indirect associations through professional development (0.06, 95% CrI [0.02, 0.10]) and technology integration (0.05, 95% CrI [0.01, 0.09]) supported H2 and H3, respectively. The sequential indirect association through professional development and technology integration was 0.03 (95% CrI [0.01, 0.05]), supporting H4. The findings indicate that AI-enabled university governance, professional development, technology integration, and perceived university-wide health education capacity are statistically interrelated. Professional development and technology integration represent distinct indirect components, while their sequential association further supports the proposed four-construct framework. Given the cross-sectional self-report design, these relationships should be interpreted as statistical associations rather than causal mechanisms. The findings are exploratory and primarily applicable to the participating educators and institutional contexts.