Orchestrating Attention: Bringing Harmony to the'Chaos'of Neurodivergent Learning States

2026-02-08

AI Summary

This study addresses the challenge of supporting neurodiverse learners—particularly those with attentional variability characteristic of ADHD—in adaptive learning systems. The authors propose AttentionGuard, a novel framework that, for the first time, defines four phenomenologically grounded attention states based on ADHD literature and leverages privacy-preserving behavioral signals to identify these states with high accuracy (87.3% on the OULAD dataset). Building on this recognition capability, the framework introduces five new UI adaptation strategies featuring bidirectional scaffolding mechanisms to counteract both under- and over-stimulation. Empirical evaluation demonstrates that AttentionGuard significantly reduces cognitive load (NASA-TLX: 47.2 vs. 62.8), improves comprehension accuracy to 78.4%, and achieves 84% agreement between its automated adaptation decisions and expert human judgments.

Technology Category

• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Machine Learning: Privacy
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• neurodivergent learning
• attention fluctuation
• adaptive learning systems
• ADHD
• cognitive load

Innovation

• attention-adaptive interface
• neurodivergent learning
• behavioral signal detection
• bi-directional scaffolding
• UI adaptation patterns