The advancement of digital education demands increasingly sophisticated approaches to address the diverse needs of learners. One of the most effective strategies is the personalization of learning based on individual competencies. This paper examines theoretical and practical methods for personalizing the learning process in digital environments, with a focus on flexibility, adaptability, and learner-centered design.

A systematic overview is presented of personalization techniques that incorporate competency frameworks to dynamically tailor learning pathways. The discussion includes the integration of competency models into learning management systems (LMS), the use of artificial intelligence and rule-based engines for adaptive content sequencing, and the implementation of diagnostic assessments to identify learners’ initial levels and progression needs. The emphasis is placed on designing learning experiences aligned with clearly defined learning outcomes, where advancement is based on mastery rather than time spent.

The paper further explores how learner profiles, performance data, and competency maps can be combined to support individualized learning experiences. Key technological components are analyzed, such as modular learning objects, real-time analytics, and interoperability standards that enable scalable and dynamic adaptation.

Despite its potential, the implementation of competency-based personalization presents several challenges, including ensuring alignment between content and competencies, maintaining transparency in adaptation logic, and protecting learner data privacy. The paper addresses these issues and proposes strategies for overcoming them through thoughtful instructional design and platform architecture.

This research aims to support educational technologists, instructional designers, and developers in building more inclusive, effective, and learner-centered e-learning systems that foster autonomy, engagement, and the development of relevant competencies.

Keywords: Competency-based learning, Personalized learning, Adaptive e-learning.