Research on AI-Driven Personalized Web Interface Adaptation Strategies and User Satisfaction Evaluation

Abstract

This research investigates AI-driven personalized web interface adaptation strategies and their impact on user satisfaction evaluation. The study addresses the growing need for intelligent interface customization in modern web applications by developing a comprehensive framework that integrates machine learning algorithms with real-time user behavior analysis. The proposed methodology combines user preference learning, dynamic interface element adaptation, and context-aware personalization algorithms to create more intuitive and efficient user experiences. Through extensive experimentation involving 450 participants across diverse demographic groups, the research demonstrates significant improvements in user satisfaction metrics, with average satisfaction scores increasing by 34.7% compared to static interface designs. The study employs multi-dimensional evaluation methods including task completion efficiency, cognitive load assessment, and subjective user feedback analysis. Statistical significance testing validates the effectiveness of the proposed adaptation strategies across different user segments. The findings contribute to the advancement of human-computer interaction research by providing empirical evidence for the benefits of AI-driven interface personalization. The research establishes a foundation for future developments in adaptive user interface technologies and offers practical insights for web developers and UX designers seeking to implement intelligent interface adaptation systems.