AI-Powered Adaptive Interface: Enhancing User Experience Through Real-Time Personalization in Digital Platforms

Amidst the accelerating evolution of AI in digital platforms, this study offers a comprehensive bibliometric analysis of 20,000 Scopus-indexed documents (1980–2024) to uncover how adaptive interfaces in Human-Computer Interaction (HCI) have transformed across three distinct phases: the Technical Phase (1980–2004), centered on rule-based systems and collaborative filtering within limited infrastructures; the Scalability Phase (2004–2019), marked by deep learning breakthroughs and contextual personalization in systems like Siri and Spotify; and the Ethical Phase (2020–2024), defined by the rise of generative AI prioritizing transparency, privacy, and user control. The findings reveal deep-rooted global research inequalities, with 76.5% of publications concentrated in the U.S., Germany, and China, and minimal representation from Africa and Latin America. Five thematic clusters were identified: blue (technical-ethical AI foundations), red (visualization and perception), yellow-green (UX evaluation), green (inclusive design), and light blue (interaction paradigms). Although individual researchers (e.g., Tscheligi, M.: 58 publications) contribute consistently, institutional dominance (e.g., Carnegie Mellon: 306 publications) underscores a productivity paradox. Practical contributions include advocating federated learning to decentralize global collaboration and enable participation from the Global South, and proposing transparency-enhancing features such as "Explain This Recommendation" in adaptive systems. Policy insights suggest implementing mandatory algorithm audits (e.g., ISO/IEC 24027) and AI literacy programs to foster user agency. This study concludes that the next frontier of adaptive interfaces depends not only on technical innovation, but also on ethical governance and inclusive design—ensuring equitable, human-centered AI that respects diverse global contexts.