The Evolution of E-Learning Platforms: From U-Learning to AI-Driven Adaptive Learning Systems - Ahmad Zain Sarnato; Windy Dian Sari; Sri Tuti Rahmawati; Rachmat Hidayat; Him Patry (Journal of Social Science Utilizing Technology, 2024)
Background. Information and communication technology development has brought significant changes in how learning is carried out, primarily through e-learning platforms. From the introduction of u-learning (ubiquitous learning) that allows access to learning anywhere and anytime to the emergence of adaptive learning systems driven by artificial intelligence (AI), this evolution continues to change the educational landscape. Purpose. This study examines the evolution of e-learning platforms from u-learning to AI-based adaptive learning systems. The main focus is understanding how each development phase has improved learning effectiveness and met individual learning needs. Method. This research uses a qualitative approach with literature study methods. Data was gathered from various academic sources, including journals, books, and conference reports discussing the evolution of e-learning. Thematic analysis is used to identify critical patterns and trends in developing e-learning platforms. Results. The results show that the evolution of e-learning has brought significant improvements in accessibility, interactivity, and personalization of learning. U-learning allows for more flexible access to education. At the same time, AI-based adaptive learning systems offer a more personalized learning experience by tailoring teaching materials and methods according to student’s needs and abilities. These findings emphasize the importance of technology in improving learning effectiveness and efficiency. Conclusion. The study concludes that e-learning platforms have evolved significantly from u-learning to AI-based adaptive learning systems, improving learning quality and effectiveness. Integrating AI in e-learning offers excellent potential for creating more personalized and compelling learning experiences. Recommendations for follow-up research include further exploring the long-term impact of adaptive learning systems and developing more advanced technologies to support more inclusive and efficient learning.