AI-Based UI Personalization in Adaptive Mobile Applications

Abstract

Artificial intelligence is increasingly being embedded within mobile applications to personalize navigation, content presentation, interaction sequences, and interface components according to individual user behavior. However, conventional personalization mechanisms frequently prioritize immediate engagement or task efficiency while paying insufficient attention to interface stability, contextual variability, and user control over automated adaptation. This study addresses this gap by conceptualizing mobile UI personalization as a constrained adaptive-learning problem in which personalization benefits must be balanced against disruptive interface changes. A context-aware personalization framework is proposed that learns from interaction history, temporal context, navigation behavior, device conditions, and explicit user preferences while maintaining a controlled degree of interface consistency. The framework combines behavioral representation learning with a contextual decision mechanism designed to determine when, where, and to what extent interface adaptation should occur.
Unlike static personalization or unrestricted reinforcement-learning approaches, the proposed research incorporates an interface-stability constraint and user override mechanism into the adaptation process.
The experimental design evaluates personalization using usability, task-efficiency, adaptation acceptance, interface stability, and computational-performance indicators rather than relying exclusively on engagement-based measures.