Determinants of Artificial Intelligence Technologies Adoption in Kenyan Universities: A Case of United States International-Africa

Purpose: This study sought to carry out analysis of factors influencing AI adoption in the education sector and focused on AI adoption determinants at the United States International University-Africa. This study sought to determine the effect of technical capability, trust, relative costs and institutional readiness on AI adoption at USIU-A. The study was founded on technology acceptance models and focused on analysis of technical capability factors, trust, cost factors and institutional readiness factors. Methodology: The study was guided by a cross-sectional research design and targeted students when collecting data since these are the main targets of AI in the university. A sample of 378 students were considered in the research. The study relied on structured questionnaires to collect data, and a Likert scale was used to code the responses. Data analysis involved coding into SPSS software, descriptive and inferential statistics including correlation and multiple regression analysis. Findings revealed that technical capability had a significant positive influence on AI adoption, explaining 25.5% of the variance. Trust factors such as privacy, reliability, and ethical use showed a weak but significant influence, accounting for 10.7%. Findings: Relative costs had an overall insignificant effect, though data management costs showed a weak positive significance. Institutional readiness factors such as culture and policy readiness had weak positive relationships, while resource readiness showed a negative correlation, with the combined factors explaining 8.4% of AI adoption. The study concluded that while cost factors are less influential, technical infrastructure, trust, and institutional readiness play important roles in AI adoption. Unique Contribution to Theory, Practice and Policy: The study recommends that USIU-A enhance AI adoption by promoting hands-on learning through workshops, pilot projects, and industry collaborations. It should partner with external organizations to access advanced technical resources and training. To build trust, the university should involve students in selecting AI tools and implement systems with clear accountability frameworks. Strategic AI adoption plans are essential, focusing on professional training, scalable platforms, and minimizing non-monetary costs. Finally, the institution should regularly review AI tools for compatibility and appoint visionary leaders to foster a culture of innovation and readiness.