Year: 2025
Type: article
Abstract: Massive Open Online Courses (MOOCs) have revolutionized digital learning by providing flexible, scalable, and cost-effective education to diverse learners worldwide. Despite their widespread adoption, MOOCs continue to face challenges such as high dropout rates, low learner engagement, and the lack of personalized learning experiences. Traditional MOOCs follow a one-size-fits-all approach, which does not cater to the unique needs and learning styles of individual students. This limitation calls for an innovative approach that leverages Artificial Intelligence (AI) to enhance personalization in online learning. This paper explores the transformative potential of AI in MOOCs by examining how adaptive learning systems, recommendation algorithms, and intelligent tutoring systems can be utilized to personalize course content and optimize learning pathways. AI-driven analytics can assess learner progress in real time, enabling the customization of learning materials and instructional strategies. Machine learning algorithms analyze learner behavior, preferences, and performance to provide tailored recommendations, helping students stay engaged and motivated. Furthermore, intelligent chatbots and virtual assistants can offer instant support, reducing learner frustration and dropout rates. The proposed conceptual framework for AI-powered personalization in MOOCs highlights key mechanisms that can improve engagement, retention, and learning outcomes. AI can bridge the gap between mass education and individualized instruction, fostering a more student-centric learning environment. By integrating AI-driven personalization, MOOCs can evolve into more effective platforms that cater to the diverse needs of global learners. Future research should focus on empirical validation of AI-powered approaches and address ethical considerations associated with AI-driven education.
Authors: Pragya Singh, Prof. Dinesh Kumar
Institution: University of Lucknow
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: bronze
OpenAlex ID: W4410410899