AI-Powered Microlearning: Translating Research Papers into Adaptive Educational Content

The integration of artificial intelligence (AI) into e-learning environments has enabled new methods for personalizing educational content, particularly through adaptive content strategies and microlearning. This paper presents a technical framework for automated content generation that leverages Large Language Models (LLMs) to process academic research and produce customized outputs tailored to varying levels of audience complexity. The proposed pipeline retrieves papers from open repositories, extracts metadata and key insights, and generates multi-format outputs including text explanations, structured summaries, slides, and narrated videos. The system incorporates complexity scaling and personality-driven adaptation to optimize engagement and accessibility. By targeting short-form media platforms, the solution facilitates the dissemination of scientific knowledge beyond expert audiences, promoting broader understanding and interaction. The results demonstrate that combining adaptive content techniques with AI-driven automation supports scalable, inclusive, and effective academic communication in digitally mediated learning environments. These findings contribute to the intersection of multimedia technologies and AI-driven pedagogy, aligning with the goals of modern hypermedia-based learning systems.