## 5 Conclusions  
This study presents an adaptive user interface system for Java programming education, combining fuzzy logic and RL to dynamically adjust hints, difficulty levels, and structured guidance. The integration of triangular membership functions and fuzzy weight thresholds that were optimized using reinforcement learning enables the system to successfully personalize UI interventions, improving learning efficiency, accuracy, engagement, and overall user experience. The evaluation demonstrated that time efficiency, error rate, success rate, and learning independence had remarkable improvement, with moderate and slow learners obtaining the most advantage. The results showed that reinforcement learning optimized the timing of UI interventions, ensuring that adaptive changes were neither too early nor too late, and as a result, maximized their influence. For slow learners, this resulted in a 28.7% increase in success rate, a 31.5% decrease in errors, and a 14.9% decrease in time spent on exercises, thereby confirming that some well-timed interventions substantially improved the learning path of the slow learners. Moderate learners also benefited substantially, while fast learners experienced smaller but still measurable improvements. This indicates that while adaptation is most beneficial to struggling students, personalized UI refinements still enhance overall engagement and efficiency. Furthermore, the system achieved a 25.0% reduction in hint requests, showing that students gradually became more self-sufficient as reinforcement learning optimized the timing and necessity of UI modifications. The usability study also confirmed that studentsrecognized and appreciated the system's adaptability, with a 29.0% improvement in perceived effectiveness of adaptive hints and a 28.1% increase in perceived UI adaptability to learning speed. These findings suggest that adaptive UI changes need to be personalized, adjusted in real time, and refined over time to maintain effectiveness in their impact. The main contribution of this work is the application of RL for fine-grained UI adjustment, which is fundamentally different from prior studies, which mainly incorporated content adaptation or static fuzzy weight tuning. Unlike traditional adaptive learning systems that utilize predefined thresholds, this system dynamically refined its classification of learner behavior, ensuring that students received just-in-time UI modifications suited to their evolving needs. The results show a considerable improvement of dynamic fuzzy weight adjustment over static adaptation models regarding learning efficacy and learners' engagement. While the results are positive, some limitations should be noted. First, the scope of adaptation was limited to UI changes related to hints, difficulty levels, and structured guidance, while other UI elements (e.g., visual organization, interface layout, accessibility customizations) were not explored. Future adaptive learning systems should consider expanding UI modifications to meet additional cognitive and accessibility needs. Second, while the RL module successfully optimized fuzzy weight thresholds, it was trained using interaction data from 100 students. While this dataset provided meaningful insights, a larger and more diverse sample would need to be analyzed to guarantee its generalization across programming level, learning style, and cognitive ability. Additionally, the evaluation was conducted over six weeks, and hence, the long-term efficacy of reinforcement learning on UI adaptation is yet unverified. These preliminary findings suggest that students became increasingly independent learners, but more research is required to see how much of this improvement is enduring over time and whether or not students end up developing long-term effective problem-solving skills. Finally, the system mainly focused on adaptive UI modifications for Java programming education, with no evidence regarding its influence in other educational contexts.