External support and university faculty's AI-TPACK: a serial mediation model of perceived ease of use, perceived usefulness, and behavioral intention

Abstract

Artificial intelligence (AI) increasingly requires university faculty to integrate technology, pedagogy, and disciplinary content, as reflected in the Artificial Intelligence-enhanced Technological Pedagogical Content Knowledge (AI-TPACK) framework. Drawing on the Technology Acceptance Model (TAM) and AI-TPACK research, this study examined how external support (ES) is associated with faculty's self-reported AI-TPACK through perceived ease of use (PEOU), perceived usefulness (PU), and behavioral intention to use AI (BI). A cross-sectional survey was conducted with 593 university faculty members in China. The measurement structure was evaluated using exploratory and confirmatory factor analyses, followed by structural equation modeling. Indirect effects were assessed using 5,000 bootstrap resamples and 95% confidence intervals. The five constructs demonstrated acceptable reliability and a distinguishable five-factor measurement structure. ES was positively associated with self-reported AI-TPACK ( β  = .238, p  < .001). The total indirect effect of ES on AI-TPACK through the specified mediators was also statistically supported (B = .287, 95% bootstrap CI [.203, .386]). The specific indirect pathway through BI alone was not supported, whereas several pathways involving PEOU and PU had bootstrap confidence intervals that excluded zero. The findings indicate that institutional support, technology-acceptance beliefs, behavioral intention, and AI-TPACK are statistically connected within the proposed structural framework. In particular, PEOU and PU appear to play important roles in the indirect associations between ES and AI-TPACK.