The Effectiveness of Artificial Intelligence in Supporting Learning Analytics within Higher Education Institutions

Abstract

This study aimed to identify the effectiveness of artificial intelligence in supporting learning analytics within higher education institutions. This was achieved by examining its current use, its effectiveness in improving electronic assessment processes and learning outcomes, its role in supporting academic decision-making, and identifying the most significant obstacles to its implementation. The study employed a descriptive survey methodology and utilized a questionnaire comprised of five main sections: usage reality, effectiveness, decision support, e-assessment and learning outcomes, and obstacles and challenges.

The results showed that the overall level of AI integration in learning analytics was high, with the decision support section and the section for improving e-assessment and learning outcomes demonstrating the highest performance. The study also revealed that AI effectively contributes to increasing the accuracy of educational data analysis, improving student academic performance prediction, and personalizing learning. Conversely, the results indicated that obstacles were of moderate magnitude, the most prominent being weak digital infrastructure, insufficient training, and poor system integration.

The study concluded with several recommendations, most notably the need to strengthen digital infrastructure, intensify training programs, and expand the use of AI in predictive analytics and decision support. This would contribute to improving the quality of higher education and meeting quality standards and academic accreditation requirements.