ARTIFICIAL INTELLIGENCE USAGE, ENVIRONMENTAL FACTORS AND STUDY HABITS AS PREDICTORS OF UNDERGRADUATES’ ACADEMIC ACHIEVEMENT IN THE UNIVERSITY OF IBADAN, NIGERIA
https://doi.org/10.83151/4jz9-kc94
This study investigated Artificial Intelligence (AI) usage, environmental factors, and study habits as predictors of undergraduates’ academic achievement in the University of Ibadan. The study was anchored to the Technology Acceptance Model (TAM) and Social Learning Theory (SLT), which stressed that the extent to which an individual considers utilising a specific system positively influences job or task performance. Ex-post facto design was employed in the study. A multistage sampling procedure was employed to select 300 undergraduates from six faculties in the University of Ibadan. Four research questions and three validated instruments guided the study, namely: Artificial Intelligence Usage, Environmental Factors and Study Habits Questionnaire (r=0.89), Environmental Factors Inventory (r=0.76), Undergraduates Achievement Scores in GES 301. Data were analysed using inferential statistics at a 0.05 level of significance and descriptive statistics. University of Ibadan undergraduates deploy AI for productive academic tasks under a conducive academic environment; relationships exist between AI adoption (r=0.375, p=0.000), environmental factor (r=0.257, p=0.000) and study habits (r=0.23, p=0.000), and undergraduates’ academic achievement in GES 301, respectively. There is a significant composite contribution of artificial intelligence adoption, environmental factors and study habits (F(3, 296)=18.115; R=0.394, R2=0.155). When undergraduates deploy artificial intelligence productively to ease their academic tasks, this will go a long way to improving academic achievement. It is therefore recommended that universities should develop clear AI usage policies for productive guidance on academic tasks and create enabling environments for optimum learning.
Keywords: Artificial-Intelligence, Environmental-factor, Study-habits, Academic Achievement in GES 301
Ikmat Olanrewaju Junaid (https://orcid.org/0000-0003-2594-6153) & Esther Oluwatoyin Okpara
ARTIFICIAL INTELLIGENCE USAGE, ENVIRONMENTAL FACTORS AND STUDY HABITS AS PREDICTORS OF UNDERGRADUATES’ ACADEMIC ACHIEVEMENT IN THE UNIVERSITY OF IBADAN, NIGERIA https://doi.org/10.83151/4jz9-kc94
This study investigated Artificial Intelligence (AI) usage, environmental factors, and study habits as predictors of undergraduates’ academic achievement in the University of Ibadan. The study was anchored to the Technology Acceptance Model (TAM) and Social Learning Theory (SLT), which stressed that the extent to which an individual considers utilising a specific system positively influences job or task performance. Ex-post facto design was employed in the study. A multistage sampling procedure was employed to select 300 undergraduates from six faculties in the University of Ibadan. Four research questions and three validated instruments guided the study, namely: Artificial Intelligence Usage, Environmental Factors and Study Habits Questionnaire (r=0.89), Environmental Factors Inventory (r=0.76), Undergraduates Achievement Scores in GES 301. Data were analysed using inferential statistics at a 0.05 level of significance and descriptive statistics. University of Ibadan undergraduates deploy AI for productive academic tasks under a conducive academic environment; relationships exist between AI adoption (r=0.375, p=0.000), environmental factor (r=0.257, p=0.000) and study habits (r=0.23, p=0.000), and undergraduates’ academic achievement in GES 301, respectively. There is a significant composite contribution of artificial intelligence adoption, environmental factors and study habits (F(3, 296)=18.115; R=0.394, R2=0.155). When undergraduates deploy artificial intelligence productively to ease their academic tasks, this will go a long way to improving academic achievement. It is therefore recommended that universities should develop clear AI usage policies for productive guidance on academic tasks and create enabling environments for optimum learning. Keywords: Artificial-Intelligence, Environmental-factor, Study-habits, Academic Achievement in GES 301