A critical study on the use of artificial intelligence in E learning technologies to enhance learners’ performance

https://doi.org/10.55214/2576-8484.v9i11.10823

Authors

  • Ali Alowayr Information Technology Department, Faculty of Computing and Information, Al-Baha University, Saudi Arabia.

The integration of artificial intelligence (AI) with e-learning technologies has significantly influenced modern educational practices by improving learners’ performance, adaptability, and engagement. E-learning, defined as the use of digital and Internet-based tools to deliver flexible learning solutions, has become an essential method for enhancing academic outcomes and developing digital competence. This study critically examines how AI-enhanced e-learning systems contribute to learners’ performance within higher education contexts. Quantitative data were collected from undergraduate students in computing and information disciplines at Al Baha University to evaluate their perceptions, experiences, and behavioral intentions toward AI-driven e-learning platforms. Statistical analysis revealed strong positive attitudes toward AI integration, with mean scores exceeding 4.3 regarding the usefulness, accessibility, and effectiveness of AI-based e-learning tools, with statistically significant P values (p < 0.05). The findings also show that AI tools promote collaboration among peers. These results confirm that AI-powered e-learning environments play a vital role in improving learning performance, efficiency, and a high level of behavioral intention to employ AI in e-learning. The study contributes empirical evidence to the importance of integrating AI technologies into e-learning systems and proposes directions for future work on personalization, adaptivity, and optimization of learning. It offers implications for educators and policymakers seeking to enhance digital learning environments.

How to Cite

Alowayr, A. (2025). A critical study on the use of artificial intelligence in E learning technologies to enhance learners’ performance. Edelweiss Applied Science and Technology, 9(11), 190–197. https://doi.org/10.55214/2576-8484.v9i11.10823

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Published

2025-10-31