Search Results for Kawa M. J. Rashid

Abstract

The integration of smart technologies into higher education is rapidly reshaping traditional teaching and learning environments. This study investigates differences in academic performance between students utilizing AI technologies and those who do not, spanning three educational levels: undergraduate, graduate, and postgraduate. Using a simulated dataset of 200 participants, the analysis employed independent sample t-tests, chi-square tests, regression models, correlation analyses, and visualizations such as scatter plots. The findings consistently demonstrate that students who adopt AI significantly outperform their peers, with notable statistical improvements across all groups. Additionally, factors like educational level and age were found to influence outcomes, with younger students and AI users showing greater consistency and higher achievement. Beyond presenting numerical data, the research contextualizes these results within modern digital learning theories, addresses challenges in technological integration, and offers practical recommendations for effectively incorporating AI tools into higher education frameworks.