THE IMPACT OF GENDER AND AGE ON HEI TEACHERS' INTENTIONS TO USE GENERATIVE ARTIFICIAL INTELLIGENCE TOOLS

Abstract

While existing research has identified key determinants of Generative Artificial Intelligence (Gen-AI) tool usage by Higher Education Institution (HEI) teachers, the moderating effects of individual characteristics such as gender and age remain underexplored. Many studies assume a homogeneous population, thereby overlooking the diverse behavioral intentions and technology perceptions among individuals, particularly in the context of higher education. This study addresses this critical gap by examining how gender and age influence the relationships between perceived utility, perceived ease of use, attitude, and subjective norms on HEI teachers' intention to adopt Gen-AI tools. By integrating these moderating factors within the frameworks of the Technology Acceptance Model (TAM) and the Theory of Planned Behaviour (TPB), this research offers a nuanced understanding of the differential impacts of gender and age on technology adoption. The findings of this study contribute significantly to the Gen-AI literature by highlighting the importance of considering individual differences in demographic characteristics when investigating technology adoption behaviors. Specifically, the study reveals that gender and age not only affect the direct determinants of Gen-AI tool usage but also moderate the strength and direction of these relationships. For instance, younger teachers may perceive Gen-AI tools as more useful and easier to use compared to their older counterparts. Practically, the study provides HEI practitioners with actionable recommendations to enhance the management and utilization of Gen-AI tools among diverse user groups. By acknowledging and addressing the unique needs and preferences of different demographic segments, HEIs can foster a more inclusive and effective adoption of Gen-AI tools. This, in turn, can lead to improved teaching and learning outcomes, as well as greater overall satisfaction with technology integration in educational settings. In conclusion, this research underscores the necessity of incorporating gender and age as critical moderating variables in studies of technology adoption. By doing so, it offers valuable insights for educators, policymakers, and researchers aiming to promote the effective and equitable use of Gen-AI tools in higher education.