AI-Driven Generative Modeling and Optimization for Ethical and Scalable Selection Systems: A Multi-Domain Scoping Review

Generative Artificial Intelligence (GAI) transforms selection and recommendation systems by enabling automated optimization, enhanced personalization, and intelligent decision-making. This scoping review surveys the integration of GAI-driven selection models across domains, such as recruitment, healthcare, education, and e-commerce, demonstrating improvements in accuracy, efficiency, and scalability. AI-enhanced personal selection, healthcare decision support systems, and generative adversarial networks (GANs) for personalized selection illustrate the impact of synthetic data generation and deep learning on optimizing user experience. Despite these advancements, ethical concerns, including algorithmic bias, transparency, and data privacy, remain important challenges. By consolidating state-of-the-art approaches and methodologies, this review maps key advancements, identifies research gaps, and proposes future directions to ensure responsible and scalable deployment of Generative AI in selection and recommendation systems.