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 study explores the link between eating disorders and online communities, analyzing user interactions on Twitter for insights into prevention and detection strategies. Is the real-life relationship reflected in social media interactions?

Group-Level Main Effects and Interactions in High-Dimensional Data.

Experimental tool for analyzing parasocial dynamics and interaction modes in conversational AI usage. Uses prompts inspired by the IMAGINE Framework (2025) to help users recognize and redirect unhealthy AI interaction patterns.

EKEEL Learning Analytics — Learning analytics model for data pattern recognition and usage behavior modeling (Deliverable D4.1)

Dual FAISS indices: semantic (text) + structural (primitives). Segment registry, query CLI. Behavior-first retrieval combining interaction mechanics + semantic similarity.