AI Reliance as Predictors of Study Habits, and Problem-Solving Skills of Science Education Undergraduates: A Case study of Imo State University Students

Abstract

This study determined how reliance on AI predicts study habits, and problem-solving skills of science education undergraduates in Imo State University. The study adopted  a survey and predictive correlation research design. The study’s population comprised 321 science education undergraduate students. The researcher utilized census sampling i.e. using the entire population as the study’s sample. For data collection, a researcher-made rating scale titled “AI reliance, Study Habits and Problem-solving skills of science education Undergraduates” was employed. Cronbach alpha statistics was used to ascertain the instrument’s reliability which yielded a coefficient of 0.81. The research questions were answered using descriptive statistics (mean and standard deviation) while the null hypotheses were tested using simple linear regression statistics at 0.05 level of significance. The findings showed that there is an above average level of reliance on AI tools among undergraduates of science education (mean = 2.51); majority of the science education undergraduates under study have good study habits (mean = 3.31);  undergraduates’ reliance on AI tools did not significantly predict their study habits (Beta =, p = 0.110); majority of the science education undergraduates under study have good problem-solving skills (mean =3.21) and their reliance on AI tools significantly predicted their problem-solving strategies (Beta = 0.468, p = 0.002). The researcher recommends that lecturers should clearly teach their students to distinguish between the tasks which need AI assistance and those which require their independent input.