Towards a new generation of intelligent, adaptive e-learning platforms

Abstract

Learning Management Systems (LMS) have evolved considerably, offering flexible and affordable access to learning and training. However, limitations persist in current solutions, such as a lack of personalisation, limited learner engagement, and often linear learning experiences that are poorly adapted to individual needs. This context highlights the need for more intelligent and adaptive solutions to meet the varied expectations of modern learners. Our work explores the integration of neural networks and deep learning for the generation of intelligent and adaptive e-learning platforms. We provide detailed modelling that demonstrates how artificial intelligence (AI) and deep learning can personalise and automate learning paths, creating more engaging and attractive learning environments. Using advanced neural network techniques, we can analyse learners’ behaviour in real time and adjust content and teaching methods to meet their specific needs. In-depth analysis of learning data provides a better understanding of learner behaviour and enables content and teaching methods to be adjusted on an ongoing basis. This approach offers a vision of a near future in which e-learning platforms become much more effective and enriching. Through detailed UML modelling, our work paves the way for truly personalised learning experiences, increasing learner motivation and engagement. In summary, the integration of AI and deep learning into LMSs promises to revolutionise the field of e-learning, making learning experiences more adaptive, effective and fulfilling.