The Rise AI-enabled E-Learning Platforms and MOOCs
DOI:
https://doi.org/10.55938/wlp.v1i5.180Keywords:
E-Learning, Massive Open Online Courses (Moocs), Personalized Learning, Smart Learning Environments, Flipped Classroom, Open Educational Resources, Blended LearningAbstract
The diversified educational programs of educational institutions sparked an international drive for e-learning integration in educational institutions during the 20th century. These incorporate diverse types of information dissemination, including sounds, visuals, and animations, to satisfy the expectations of their intended audience. Technology has enabled learners to get knowledge outside of the classroom, allowing them to experiment with different learning methods. Given their low cost, dynamic state, directions, and technological advances, free e-learning platforms are the most appealing alternatives. A review of research articles, journals, and e-books is utilized in this work to explore the objective, execution, recipient group, results participation criteria, and barriers of Massive Open Online Courses (MOOCs). It emphasizes the significance of MOOCs in 21st-century expansion, encompassing psychological, exclusive, and professional development, as well as the necessity for greater understanding among learners, educators, and prospective teachers. Considering previous frameworks and participant evaluations, the research study investigated MOOC platform architectures through various categories of collaboration and interaction. A tree structure was established to compare the findings, and it revealed the overall collaboration prospects in platforms are significantly less than interaction opportunities. This emphasizes the need of enhanced platform design and engagement opportunities. This study explores the influence of machine learning on training student satisfaction and ease of operation. It analyzes user preferences and reveals the intricacies of online and artificial intelligence (AI)-enabled mobile applications. Data gathered through user groups and survey questionnaires. According to the investigation, web-based programs offer more potential for e-learning development and improvement than mobile applications. The study finishes offering a conceptual framework enabling machine learning and e-learning applications which employ artificial intelligence approaches.
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Copyright (c) 2025 Meenakshi Sharma, Neh Sharma
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