Adaptive learning systems optimize content delivery based on performance metrics but ignore the dynamic attention fluctuations that characterize neurodivergent learners. We present AttentionGuard, a framework that detects engagement-attention states from privacy-preserving behavioral signals and adapts interface elements accordingly. Our approach models four attention states derived from ADHD phenomenology and implements five novel UI adaptation patterns including bi-directional scaffolding that responds to both understimulation and overstimulation. We validate our detection model on the OULAD dataset, achieving 87.3% classification accuracy, and demonstrate correlation with clinical ADHD profiles through cross-validation on the HYPERAKTIV dataset. A Wizard-of-Oz study with 11 adults showing ADHD characteristics found significantly reduced cognitive load in the adaptive condition (NASA-TLX: 47.2 vs 62.8, Cohen's d=1.21, p=0.008) and improved comprehension (78.4% vs 61.2%, p=0.009). Concordance analysis showed 84% agreement between wizard decisions and automated classifier predictions, supporting deployment feasibility. The system is presented as an interactive demo where observers can inspect detected attention states, observe real-time UI adaptations, and compare automated decisions with human-in-the-loop overrides. We contribute empirically validated UI patterns for attention-adaptive interfaces and evidence that behavioral attention detection can meaningfully support neurodivergent learning experiences.

翻译：自适应学习系统基于性能指标优化内容呈现，却忽视了神经多样性学习者特有的动态注意力波动。我们提出AttentionGuard框架，该框架通过隐私保护的行为信号检测参与-注意力状态，并据此调整界面元素。我们的方法基于ADHD现象学建模了四种注意力状态，并实现了五种新颖的UI适配模式，包括针对刺激不足和过度刺激的双向支架式调节。我们在OULAD数据集上验证了检测模型，获得87.3%的分类准确率，并通过在HYPERAKTIV数据集上的交叉验证证明了其与临床ADHD特征的相关性。一项针对11名具有ADHD特征的成年人的Wizard-of-Oz研究发现，自适应条件下的认知负荷显著降低（NASA-TLX：47.2对比62.8，Cohen's d=1.21，p=0.008），理解能力得到提升（78.4%对比61.2%，p=0.009）。一致性分析显示向导决策与自动分类器预测的吻合度达84%，支持了系统部署的可行性。本系统以交互式演示形式呈现，观察者可查看检测到的注意力状态，观察实时UI调整，并比较自动决策与人工介入的差异。我们贡献了经过实证验证的注意力自适应界面UI模式，并证明行为注意力检测能有效支持神经多样性学习体验。