Abstract

Generative Artificial Intelligence (AI) refers to a class of models designed to autonomously produce new content, including text, images, audio, and video, by learning underlying patterns from large datasets. Fueled by advances in deep learning, generative models such as Generative Adversarial Networks (GANs), VariationalAutoencoders (VAEs), Autoregressive Language Models, and transformer architectures have revolutionized digital content creation. These models are capable of scaling creativity by enabling automated generation of high‑quality content, reducing the labor and time required by human creators. Applications span journalism, advertising, entertainment, design, education, and scientific communication. At the same time, this rapid progress raises ethical, legal, and socio‑technical challenges surrounding originality, authenticity, bias, and misuse. This paper explores the landscape of generative AI models for digital content creation, synthesizing developments in architectures, training paradigms, and deployment frameworks. It examines strengths, limitations, and opportunities, offering a comprehensive methodology for evaluating model performance, human‑AI collaboration, and responsible deployment. By consolidating research from foundational works up to 2021, the study aims to provide a cohesive understanding of generative AI’s transformative role in automated and intelligent content creation.