Large language models and innovative work behavior in higher education curriculum development

The growth of generative artificial intelligence (GAI), remarkably, Large Language Models (LLMs) such as ChatGPT, converts the educational environment by empowering intelligent, data-driven education and curriculum design innovation. This study aimed to assess the integration of LLMs into higher education to foster curriculum design, learning outcomes, and innovative work behaviour (IWB). Specifically, this study investigated how LLMs' perceived usefulness (PU) and perceived ease of use (PEOU) can support educators to be engaged in IWB-idea generation (IG), idea promotion (IP), opportunity exploration (OE), and reflection (Relf)-employing a web-based survey and targeting faculty members. A total of 493 replies were obtained and found to be valid to be analysed with partial least squares structural equation modelling (PLS-SEM). The results indicated that PU and PEOU have a significant positive impact on the four dimensions of IWB in the context of LLMs for curriculum development. The evaluated model can assist in bridging the gap between AI technology acceptance and educational strategy by offering some practical evidence and implications for university leaders and policymakers. Additionally, this study offered a data-driven pathway to advance higher education IWB through the adoption of LLMs.