AI-Driven Learning-Style Detection for Personalized MOOC Content Delivery in Lifelong Learning

Zhonglin Zhao

doi:10.54097/7d530x88

Authors

Zhonglin Zhao

DOI:

https://doi.org/10.54097/7d530x88

Keywords:

Artificial Intelligence; MOOCs; Learning Styles; Personalization; Lifelong Learning; Adaptive Learning.

Abstract

To investigate how artificial intelligence (AI) techniques detect learners’ individual learning styles and enable customized content delivery in Massive Open Online Courses (MOOCs) for lifelong learners. This review synthesizes findings from peer-reviewed studies on AI-driven personalization in MOOCs covering adopted methods, empirical results and gaps. The review shows that AI algorithms (e.g., neural networks, decision trees, clustering) can automatically identify learning style preferences by analyzing learners’ online interactions, often with high accuracy (frequently above 90%). Incorporating learning-style detection into MOOCs has the potential to enhance learner engagement, satisfaction and possibly learning outcomes. Globally, studies span diverse regions and platforms, from international MOOC providers to local university e-learning systems, demonstrating widespread interest. AI-driven personalization is contrasted with traditional one-size-fits-all MOOC delivery, showing clear advantages in catering to learners’ diverse needs. AI-based learning style adaptation in MOOCs is an emerging field yielding promising results for lifelong learning. However, empirical evidence of long-term learning gains is still limited and the approach faces challenges (e.g., data privacy, the validity of learning style models). Future research should address these limitations through larger-scale longitudinal studies and explore advanced adaptive strategies (including multimodal and generative AI) to fully realize personalized lifelong learning at scale.

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