Leveraging Context-Aware Machine Learning to Enhance Personalization in Adaptive User Interfaces: A Study on User Engagement and Experience in Human-Computer Interaction

Abstract

The advent of context-aware machine learning (CAML) has revolutionized the development of adaptive user interfaces (AUIs), enabling a higher degree of personalization that enhances user engagement and experience in human-computer interaction (HCI). This research paper explores the integration of CAML into AUIs, focusing on its implications for user engagement and experience. By employing a mixed-methods approach, including qualitative user studies and quantitative data analysis, this study investigates how context-aware systems can adapt to individual user preferences, behaviors, and environmental factors. The findings suggest that leveraging CAML in AUIs significantly improves personalization, leading to increased user satisfaction and engagement. Recommendations for future research and practical applications in various domains are also discussed.