The role of Generative AI in educational tests and individual learning is addressed in this chapter in a bibliometrics analysis. The study employs co-citation analysis and coward analysis in efforts targeting research trends, challenges, and key themes in integrating the AI technologies in educational environments. The total number of articles retrieved from Scopus using a specific search query aiming at Generative AI in education, personalized learning, and assessment thoroughly was 1434 and were published between 2005 and 2025. The collected data was exported and analyzed with VOSviewer in order to produce visual maps, discover influential literature and study keyword co-occurrence, and co-citation networks. The findings indicate that there is an increased role of machine learning, natural language processing as well as adaptive learning systems in personalizing educational assessment and content delivery. Research clusters of prominence as pointed out by the study includes adaptive learning technologies, Generative AI applications such as ChatGPT and Federated Learning in education practice. Ethical issues that include AI bias, data privacy, and algorithmic fairness are also talked about. Finally, the chapter concludes with the directions for future studies that include the ethical, technical, and cross-cultural impacts of AI-powered personalized education, with tips on how to achieve transparency, scalability, and equity in terms of introducing AI systems.