In today’s educational landscape, from traditional schools to MOOC platforms, the prevalent model is a one-size-fits-all approach to learning content, often overlooking the unique needs and learning paces of individual students. This gap between the ideal personalized instruction by a team of personal trainers and the practicalities of standardization presents significant challenges, including diminished engagement and understanding. My PhD research proposes a Knowledge Graph-based application of core Technology Enhanced Learning (TEL) components, aiming to bridge this divide with a cost-effective method targeted to individual learning objectives and paths. The core elements of the approach are structured around five key components: Knowledge Graphs (KGs), Large Language Models (LLMs), Flashcards, Visualization of Dynamic Competence Maps (DCMs), and a Quality Assurance (QA) review and feedback workflow. The approach will collect empirical data on students needs and misconceptions which allow to apply learning analytics for continuous improvement of the learning material. We hypothesize that this approach will provide a viable method for digitization and entering into a quality improvement cycle based on the rating results, offering a concrete solution for the transition from traditional learning materials.