Perceived Ease of Use and Usefulness in AI-Generated Content Adoption: Gender as a Moderator

Abstract

Artificial Intelligence generated content (AIGC) is rapidly reshaping higher education. Guided by the Technology Acceptance Model (TAM), this study examines how perceived ease of use (PEOU) and perceived usefulness (PU) predict Chinese faculty members’ AIGC use behaviour, with perceived self-efficacy (PSE) and institutional support (IS) modelled as external variables. Survey data from 295 faculty members was analysed and structural equation modelling was employed to test the hypothesized relationships. The results reveal that both PEOU and PU are critical predictors of AIGC adoption, while PSE and IS significantly enhance faculty perceptions of ease of use and usefulness, thereby facilitating actual usage behaviour. The model accounted for 55.2% of the variance in AIGC adoption. Furthermore, gender differences emerged as a significant moderating factor, where female faculty members were more strongly influenced by PEOU, whereas male faculty members with PU. These findings highlight the importance of strengthening faculty confidence and digital competence through differentiated training and institutional support mechanisms. The study extends TAM in an AIGC context and offers practical guidance for inclusive AIGC adoption strategies in higher education.