Journal Article

Determinants of Applying Artificial Intelligence to Improve the Quality of E-Learning

Yehia, Engy Ahmed, El-Khouly, Sayed Mahmoud, Mohammed, Aya Yasser Sweify

2024

Overview

Purpose: Determine the driving and hindering forces of applying artificial intelligence to improve the quality of E-learning in Ain Shams University. Approach of the study: The study was based on descriptive and analytical method to study the relationship between the variables. Population and Study Sample: The empirical study was applied in Ain Shams University, the sample was taken from 5 Theoretical and Practical colleges such as Medicine, Engineering, Law, Business, and Arts by collecting 383 questionnaires from students. Research Problem: Since 2019, with the spread of Corona virus, universities began to replace traditional learning methods with E-learning, which led to immediate disruption to the lives of many. The problem of the study lies in that this disruption is due to some of the problems that faced e-learning, such as weak infrastructure, technological sufficiency challenges, and students' isolation challenges and lack of students' interaction with their instructors appropriately. Some students face difficulty in dealing with modern systems and lack of experience and knowledge in the field of e-learning. During the collection of the initial sample of the questionnaire, the researcher found that some students responded with disagree and others with neutral about achieving the quality of e-learning, which indicates a lack of uniformity of opinion and the need for research into whether it needs further development. Findings: Reliability and validity of the research variables. As a result, it represents the targeted population well, and can be used in the subsequent statistical analysis. By conducting simple regression analysis, we found that, there is a statistically significant effect of the driving forces on applying artificial intelligence to improve e-learning quality in Ain Shams University, which are performance expectancy, effort expectancy, and social influence, while multiple linear regression analysis found a significant effect only of Performance expectancy, and effort expectancy on E-learning quality. The main barrier for applying artificial intelligence is student isolation challenges only, as all other barriers showed no significant effect on E-learning quality.

• Publisher: جامعة عين شمس - كلية التجارة
• Subject: التعلم عن بعد / المنصات الرقمية / تكنولوجيا المعلومات / جودة التعليم