{"repo":"AmirhosseinHonardoust/Mobile-AI-Satisfaction-Behavior-Aanalysis","free":true,"listed":false,"github":"https://github.com/AmirhosseinHonardoust/Mobile-AI-Satisfaction-Behavior-Aanalysis","clone":"git clone https://github.com/AmirhosseinHonardoust/Mobile-AI-Satisfaction-Behavior-Aanalysis.git","description":"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.","language":null,"stars":19,"topics":["behavioral-analytics","behavioral-science","cognitive-load","data-science","data-visualization","explainable-ai","feature-engineering","hci","human-ai-interaction","interaction-analysis"],"license":"MIT","category":"machine-learning","readme_excerpt":"Why Mobile Users Rate AI Lower: A Comprehensive Behavioral, Cognitive, and Machine Learning Explainability Analysis Abstract Despite identical AI models and identical query content, users interacting through mobile devices consistently produce lower satisfaction ratings than users on desktop, tablet, or smart-speaker platforms. This paper investigates the underlying mechanisms of this phenomenon using a real behavioral dataset of 300 AI assistant interactions, a supervised machine learning classifier, and SHAP explainability methods. The findings reveal that mobile usage introduces a constellation of behavioral constraints , cognitive limitations , and environmental pressures that produce systematically lower satisfaction, even when AI performance is unchanged. Through rigorous model interrogation, we isolate device modality as a dominant negative predictor and explore how human cognitive architecture interacts with device ergonomics to shape satisfaction outcomes. --- 1. Introduction User satisfaction is a complex, multi-dimensional signal shaped by: technical performance cognitive effort device ergonomics emotional state environment time pressure attentional load When we treat satisfaction as a target variable for machine learning, we quickly discover that behavioral and contextual features often outweigh the model's computational accuracy. Among these contextual features, device type emerges as a dominant factor. In multiple datasets (including yours), mobile users rate AI","default_branch":null,"files":null,"tree":[],"storefront":"/r/AmirhosseinHonardoust","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/AmirhosseinHonardoust/Mobile-AI-Satisfaction-Behavior-Aanalysis/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}