###### Abstract  
Adaptive educational systems are essential for addressing the diverse learning needs of students by dynamically adjusting instructional content and user interfaces (UI) based on real-time performance. Traditional adaptive learning environments often rely on static fuzzy logic rules, which lack the flexibility to evolve with learners' changing behaviors. To address this limitation, this paper presents an adaptive UI system for educational software in Java programming, integrating fuzzy logic and reinforcement learning (RL) to personalize learning experiences. The system consists of two main modules: (a) the Fuzzy Inference Module, which classifies learners into Fast, Moderate, or Slow categories based on triangular membership functions, and (b) the Reinforcement Learning Optimization Module, which dynamically adjusts the fuzzy membership function thresholds to enhance personalization over time. By refining the timing and necessity of UI modifications, the system optimizes hints, difficulty levels, and structured guidance, ensuring interventions are neither premature nor delayed. The system was evaluated in educational software for Java programming, with 100 postgraduate students. The evaluation, based on learning efficiency, engagement, and usability metrics, demonstrated promising results, particularly for slow and moderate learners, confirming that reinforcement learning-driven fuzzy weight adjustments significantly improve adaptive UI effectiveness. f [table]capposition=top  
modify content presentation based on student performance, thus enhancing both ease of use and instructional efficacy. In addition to colors and layouts, the interface incorporates adaptive hints that show up or go away depending on student need and difficulty level adjustments that introduce more difficult challenges if students are performing well [5]. It also includes step-by-step guidance that offers greater assistance to struggling students, interactive feedback such as tooltips or highlighted mistakes, and layout features that highlight critical information and remove distractions [6]. These UI adaptations are used to foster the overall interaction in such a way that it is not only visually accessible but also pedagogically sound [7]. A well-constructed adaptive user interface guarantees the appropriate amount of support and challenge, providing opportunities for pupils to solve problems independently without risking frustration or disconnection when the material becomes too hard. Artificial intelligence methods have gained a lot of interest lately due to the fact that they allow educational software to evaluate the effectiveness of learning methods in real-time and adapt instruction dynamically [8]. One effective approach is the use of fuzzy logic, which provides a way to handle uncertainty and imprecise data, which is common in human learning [9]. Fuzzy weights allow the system to make decisions about adapting the UI by modeling student engagement, performance, and response time. Instead of applying rigid rules that classify students into strict categories, fuzzy weights make it possible to assess learning behavior on a spectrum, allowing for more gradual and personalized adjustments. This means that the system does not assume, for example, that if a student spends a long time on an exercise, he or she is having trouble, but rather uses fuzzy logic to suggest that a student who takes an inordinate amount of time is \"probably\" having difficulties and acts on that assumption. This adaptive approach makes educational software more intelligent, user-friendly, and supportive without over-intervening. While fuzzy weights improve adaptivity, static fuzzy rules may not always provide optimal support for every student [10]. Since learners' behaviors change over the course of learning, this means existing adaptation mechanisms need to be improved continuously. A fuzzy weighted system based on predetermined conditions and goals fails to capture the changing learning status of students [11].