The post-pandemic context has made learning management systems (LMS) essential tools for teaching. This change requires adapting educational practices through the use of advanced technologies, which enable the provision of personalized educational resources tailored to the individual preferences and characteristics of students. From this premise, this paper explores how LMS should adapt to students’ individual characteristics and needs to improve their motivation and experience. Focusing on the use of advanced technologies such as Artificial Intelligence (AI) and Machine Learning (ML), the creation of personalized educational environments that adjust teaching resources and strategies according to users’ preferences and behaviors is proposed. The paper addresses the importance of collecting explicit and implicit data to tailor the LMS to students’ preferences. Two main approaches to LMS adaptability are highlighted: the student model, which adjusts content and navigation according to the learner’s learning style, and the user model, which customizes the interface presentation, focusing on usability and user experience. In addition, theories of learning styles are mentioned, and the use of advanced technologies such as facial expression recognition to optimize the user experience. The proposal presented suggests an adaptive model of Graphical User Interface (GUI) in LMS systems, using AI and the recording of emotions and facial expressions. The process has three components: the user model, the domain model, and the adaptation model. Usability assessments validate the implementation of this model. A mixed Design-Based Research (DBI) and User-Centered Design methodology is proposed, which ensures an iterative approach and continuous system improvement.