IMPACT OF ARTIFICIAL INTELLIGENCE (AI) ON THE QUALITY OF RESEARCH BY ACADEMIC STAFF OF UNIVERSITIES IN TARABA STATE

Abstract

The role of Artificial Intelligence (AI) in academic research has gathered significant attention in recent years. This transformative technology, powered by machine learning procedures and data analytics, is transforming the research landscape. This paper examined the impact of Artificial Intelligence on the quality of research by academic staff of universities in Taraba State, Nigeria. Three 
research questions and one null hypothesis guided the study. The descriptive survey research design was adopted for the study. The population of this study consisted of all the 920 academic staff of Federal University Wukari. Stratified random sampling technique was used to select 150 academic staff, stratified along male and female, as respondents who participated in the study. The instrument used for data collection was researcher-developed questionnaire titled ―Impact of Artificial Intelligence on Academic Research Questionnaire (IAIARQ)‖ on a modified four point Likert scale. The precision and internal consistency of the instrument was determined using Cronbach reliability method which gave rise to a coefficient ranging from 0.76 - 0.82. The instrument was face validated by two experts from the Faculty of Education Federal University, Wukari; with a validation index of 0.85. The data collected was analyzed using percentage scores, mean, and standard deviation. Chi-Square (X2) statistic was used to test the hypothesis at 0.05 level of significance. The result of the analysis revealed that majority of academic staff do not use Artificial Intelligence for their research, due to inadequate awareness, however they agree that it is a veritable tool for research. It was recommended that universities in Taraba State through the Ministry of Innovation and Digital Economy should organize workshops for academic staff 
to train them on how to use AI for academic research in order to improve research outcomes and reduce stress associated with data analysis