Determinants of lecturer readiness to adopt generative AI in higher education: survey evidence from UTAUT and self-determination theory

Generative artificial intelligence (GAI) is reshaping higher education, yet lecturers’ readiness remains under-examined. This study integrates the Unified Theory of Acceptance and Use of Technology (UTAUT) with Self-Determination Theory (SDT) and individual attributes (AI literacy, teaching values, personal innovativeness) to explain lecturers’ behavioural intention and GAI use. A cross-sectional survey of 651 university lecturers in mainland China measured UTAUT constructs (performance expectancy, effort expectancy, social influence, facilitating conditions), SDT needs (autonomy, competence, relatedness), and individual attributes. Confirmatory factor analysis and covariance-based structural equation modelling assessed measurement quality and structural paths; bootstrapped mediation tested indirect effects via intention, and latent-interaction moderation examined whether SDT strengthened antecedent–intention links. SDT was the strongest predictor of intention; UTAUT constructs were also significant. Teaching values and personal innovativeness showed positive effects, and AI literacy was positively associated with use/intention, which strongly predicted use, with facilitating conditions and SDT also showing direct effects. Findings conclude the value of professional development in Technology Enhanced Learning (TEL) that builds faculty AI literacy and competence, which supports autonomy and collegial relatedness, and must be underpinned by reliable institutional infrastructures to accelerate responsible GAI integration.