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

TL;DR

This paper introduces AttentionGuard, a privacy-preserving framework that detects attention states in neurodivergent learners to adapt interfaces, validated through datasets and user studies, improving engagement and reducing cognitive load.

Contribution

The paper presents novel attention detection models and UI adaptation patterns specifically designed for neurodivergent learners, validated through datasets and user studies.

Findings

Achieved 87.3% classification accuracy on OULAD dataset.

Significantly reduced cognitive load in adaptive condition (NASA-TLX: 47.2 vs 62.8).

Improved comprehension scores with adaptive UI (78.4% vs 61.2%).

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.