Enhancing Adaptive E-Learning with Generative AI: Expanding the Horizon Beyond Recommendation Systems

In this paper, we introduce a pioneering approach to e-learning recommendation systems by harnessing the capabilities of Generative AI. Traditional recommendation methods have been trained on small-scale special-purpose datasets and they often fall short when deployed in e-learning applications, due to their inability to capture evolving user preferences and content diversity. In this work, we instead turn to Generative AI systems such as large language models (LLMs), which are extremely powerful general-purpose systems trained over Internet-scale data. We design specific prompting strategies that enable our Generative AI-based recommendation system to dynamically adapt to learners’ evolving preferences as they progress through course content, transcending the limitations of state-of-the-art systems, which still rely heavily on collaborative and content-based filtering. Beyond mere content suggestions, our system engages learners in real-time, providing personalized explanations and practice materials, and even generating bespoke learning resources. Experimental validation on real-world data demonstrates remarkable improvements in user engagement and learning outcomes compared to conventional systems. This Generative AI-driven adaptive e-learning approach not only enhances recommendations but also redefines the e-learning experience itself, marking a significant leap forward in creating a more adaptive, engaging, and enriching educational environment.