Artificial Intelligence Tools and Performance of Academic Staff in Federal Universities, South-East Nigeria

Abstract

This study examined the effect of artificial intelligence (AI) tools on the performance of academic staff in Federal Universities, South-East Nigeria. The study was motivated by growing uncertainty over whether the increasing integration of AI into academic work is genuinely enhancing staff performance, given persistent challenges such as unequal access to AI infrastructure, limited AI literacy among lecturers, and the perceived complexity of some AI systems. Specifically, the study examined the effect of access to AI tools on teaching effectiveness of academic staff; ascertained the effect of AI literacy on research productivity of academic staff; and evaluated the effect of perceived flexibility of AI systems on the adoption rate of AI tools among academic staff in Federal Universities, South-East Nigeria. Three research questions and three hypotheses were formulated in line with the study objectives. The study was anchored on the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). A descriptive survey research design was adopted. The population comprised 11,640 academic staff drawn from five Federal Universities in South-East Nigeria, with a sample size of 375 determined using the Krejcie and Morgan (1970) table. Data were analyzed using descriptive statistics and simple linear regression at a 0.05 level of significance with the aid of SPSS version 23. Findings revealed that all variables (access to AI tools had a strong, positive, AI literacy and perceived flexibility of AI systems) had very strong, positive, and statistically significant effect on the adoption rate of AI tools. The study concluded that access to AI tools, AI literacy, and the perceived flexibility of AI systems are critical determinants of academic staff performance in Federal Universities in South-East Nigeria. It was recommended that universities sustain investment in AI infrastructure, organize regular AI training and capacity-building programmes, and prioritise the design and adoption of user-friendly AI platforms for academic use.