International Research Journal of Education and Technology

Generative Artificial Intelligence (Generative AI) is revolutionizing the way machines generate content like text, images, audio, and code. While traditional AI models are restricted to analyzing data, generative AI models learn patterns from large datasets and generate new, realistic data. This paper provides a brief introduction to prominent generative AI models like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Transformers, and Diffusion Models. We discuss their functioning, applications in the healthcare, education, and software industries, and current trends like multimodal and agent-based AI models. The paper also discusses the ethical issues of bias, misinformation, and privacy, while providing a roadmap for future research in responsible and scalable generative AI.

Keywords—Generative AI, Deep Generative Models, GANs, VAEs, Transformers, Diffusion Models, Ethical AI