## 4 Evaluation
### Discussion
These results reinforce the idea that adaptive UI interventions successfully guided students while promoting independent problem-solving. Unlike traditional tutoring systems where hints and support are either always available or fully controlled by the user, the proposed system intelligently adjusted interventions based on real-time student behavior, ensuring that students received appropriate support at the optimal time. The usability study further confirmed that students perceived the adaptive UI as highly effective, with a 28.1% increase in perceived UI adaptability and a 29.0% increase in the effectiveness of adaptive hints. This suggests that students not only benefited from UI modifications but also recognized and appreciated the system's ability to personalize their learning experience. The improvement in cognitive load ratings (+31.0%) indicates that reinforcement learning successfully optimized fuzzy weight thresholds, reducing unnecessary distractions and streamlining the learning process. These findings contribute to the broader field of adaptive learning and AI-driven personalization, demonstrating that reinforcement learning can successfully improve the accuracy of fuzzy weight classifications, leading to more contextually relevant UI modifications. Unlike previous adaptive learning models that rely on predefined, static thresholds, the proposed system dynamically adjusts UI interventions in response to real-time learning behavior, ensuring a higher degree of personalization and continuous optimization. A key advantage of integrating reinforcement learning with fuzzy logic is that the system becomes increasingly effective over time, learning from student interactions to refine its decision-making process. Traditional adaptive systems often rely on pre-programmed heuristics, which may not account for individual differences or evolving learning patterns. In contrast, the reinforcement learning approach allows the system to self-improve, ensuring long-term adaptability and effectiveness. Compared to previous studies on adaptive educational systems [47; 48; 49; 50], this research introduces a novel reinforcement learning-driven approach to dynamically adjusting fuzzy weights, leading to more precise and context-aware UI interventions. Prior research on fuzzy logic in adaptive learning has demonstrated that fuzzy weights can effectively model student uncertainty, but many of these approaches have relied on static membership functions, which fail to adapt to changing learning behaviors. Furthermore, reinforcement learning has been widely used in educational AI for content sequencing and recommendations, but its direct application to fine-tuning UI elements in real time remains underexplored. While previous studies have successfully applied reinforcement learning to optimize learning pathways, this research demonstrates that reinforcement learning can also enhance micro-level UI adjustments, such as when to display hints, modify difficulty levels, or provide structured guidance. The success rate improvements observed in this study, particularly the 28.5% increase for slow learners, are notably higher than those reported in prior work using static fuzzy logic models (e.g., [48; 49]), which typically show improvements in the range of 10-15%. This suggests that the ability to dynamically adjust fuzzy weights based on real-time learning behavior leads to significantly greater benefits for struggling learners. Additionally, previous research (e.g., [47; 50]) has shown that adaptive UI interventions can improve engagement, but the fine-grained optimization achieved through reinforcement learning in this study surpasses the typical engagement gains found in static adaptive systems. The 25.0% reduction in hint requests observed in this study is higher than what has been reported in previous adaptive hinting systems [51], indicating that dynamically optimizing the conditions under which hints appear leads to greater self-sufficiency in students.