The rise of Artificial Intelligence (AI) and Large Language Models (LLMs) has created new opportunities for intelligent educational systems, particularly in customizing and personalizing learning content. This paper explores strategies, methodologies, and lessons learned in developing adaptive educational content using LLMs within Moodle, a widely used Learning Management System (LMS). Drawing on CARNET’s large-scale educational transformation initiatives, the paper demonstrates how LLMs dynamically adapt learning materials to meet specific user needs, knowledge gaps, and learning styles. By leveraging AI-driven content that responds to student performance during assessments, LLMs generate tailored learning materials and personalized pathways. The process includes training and fine-tuning LLMs on domain-specific knowledge, developing content templates, and integrating them into the LMS. This enables differentiated learning experiences unique to each user’s progress. Key challenges, including technical complexities, human factors, and cross-stakeholder collaboration, are also discussed. Practical insights are provided on content relevance, system integration, user adoption, and the role of intelligent tutoring systems (ITS) in supporting adaptive learning at scale. This paper offers a roadmap for institutions to incorporate LLMs and AI-driven customization into their learning ecosystems.