AmirhosseinHonardoust/Mobile-AI-Satisfaction-Behavior-Aanalysis
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.
This project helps product managers, UX researchers, and AI strategists understand why users rate AI systems lower when interacting on mobile devices compared to desktops or smart speakers. By analyzing behavioral data and AI assistant interactions, it identifies the specific factors (like cognitive load and interaction friction) that lead to reduced satisfaction. The output provides clear explanations and evidence to inform product design and user experience improvements for mobile AI.
Use this if you are developing or managing AI products and observe lower user satisfaction ratings specifically from mobile users, and you need to understand the underlying behavioral and cognitive reasons.
Not ideal if your AI system does not involve user satisfaction ratings, or if you are primarily interested in technical AI model performance rather than human-AI interaction dynamics.
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MIT
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Last pushed
Dec 07, 2025
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