Deep behavioral and machine learning analysis explaining why mobile users systematically report lower satisfaction with AI systems. Includes SHAP explainability, cognitive load modeling, device-context effects, interaction metadata analysis, and end-to-end reproducible research code and visuals.
python data-science machine-learning random-forest mobile-analytics data-visualization hci feature-engineering user-experience model-interpretation explainable-ai ux-research shap behavioral-analytics human-ai-interaction behavioral-science cognitive-load interaction-analysis satisfaction-modeling context-aware-ml
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Updated
Dec 6, 2025