Generative Artificial Intelligence and Academic Performance

Abstract

The purpose of the study was to investigates the relationship between generative artificial intelligence (GAI), academic performance (AP) and smart learning environment (SLE) as a mediator. A convenience sampling technique was used to select a sample size of 456 respondents. Primary data was collected using a self-administered questionnaire and analysed using descriptive and inferential statistics. Partial least squares-structural equation model (PLS-SEM) was employed to analyse the structural model and determine the direct connections between the different elements. The results establish that generative artificial intelligence has a positive and significant influence on smart learning environment and academic performance (β = 0.523, t = 10.178, p < 0.000); β = 0.387, t = 7.353, p < 0.000 respectively). Simultaneously, smart learning environment partially mediates between the generative artificial intelligence and academic performance among university students (β = 0.06, t = 1.19, p < 0.234). The results of this study contributes to the current academic discourse on technology-enhanced education by showing that generative artificial intelligence have a positive impact on students’ academic performance.