Next-Generation AI-Powered Content Personalization: Adaptive Generation Models for Real-Time User Engagement

Abstract

Next-Generation AI-Powered Content Personalization: Adaptive Generation Models for Real-Time User Engagement

Rahul Modak, Mohamed Abdul Kadar Mohamed Jabarullah, Praggnya Kanungo
Independent Researcher, USA

ABSTRACT
Recent trends in the development of AI technologies have transformed content personalization into the foundation of contemporary digital experiences. The article explores the theme of AI-driven content personalization, referencing adaptive models that aim to engage users online. As users and their habits evolve, it has become increasingly essential to have dynamic systems that can adapt the way content is delivered. The study outlines the approaches used in adaptive AI applications, with particular mention of the machine learning approach, which adapts content according to updated interactions. The study shows through the case studies and performance assessment that such adaptive models are very effective in enhancing user response and retention when compared to traditional approaches. Some critical findings suggest that the AI-powered system offers a more personalized and seamless experience for users; however, it still faces issues related to a lack of ethical consideration, privacy concerns, and real-time processing. To sum up, the article suggests viable means by which it is possible to increase the scalability, fairness, and transparency of AI models in the personalization of content.