Effectiveness of AI-Predictive Analytics in Enhancing Academic Research among Postgraduate Educational Management Students in Universities in Rivers State

Abstract

The study was conducted to find out how effective AI-Predictive Analytics enhance Academic Research among Postgraduate Educational Management Students in public universities in Rivers State. The study was guided by two specific objectives, two research questions and two hypotheses. A descriptive survey design was adopted with a population of 431 postgraduates’ students of Educational Management from three public universities which were entirely used as sample for the study because of the manageable size. Data for the study were collected through a questionnaire validated by three experts and with reliability coefficient of 0.81 obtained through test retest method. Data gathered were analysed using mean to answer research questions and analysis of variance for testing the hypotheses at 0.05 level of significance. The result of the study shows that there is no significant difference in respondents' mean scores of state-owned universities and the federal university on data collection using AI-Predictive Analytics in enhancing academic research among postgraduate Educational Management students in universities in Rivers State and that there is no significant difference in respondents' mean scores of state-owned universities and the federal university on data organization using AI-Predictive Analytics in enhancing academic research among postgraduate Educational Management students in universities in Rivers State. Based on the result, it was recommended that Universities should provide targeted training and support to students and researchers on effectively using AI-Predictive Analytics. This training should address common challenges related to data accuracy and error minimization to improve the overall effectiveness of AI tools in data collection and institutions should integrate AI tools more seamlessly into research workflows to help address concerns about inconsistency in data organisation.