One kind of artificial intelligence technology called generative AI is used to create new text, picture, audio, and video material. It may be used for many different things in education, such creating material, enhancing data, personalizing learning, simulating situations, and providing training. It also raises moral questions about prejudices, veracity, false information, intellectual property, loss of employment, and potential future developments like more realism and responsiveness. Content creation, personalized learning, administrative work automation, interactive learning environments, feedback and evaluation, natural language processing (NLP), forecasting and prediction, and collaborative learning are some of the educational applications of generative AI approaches. These technological advancements are intended to improve educational opportunities, streamline administrative duties, and provide individualized course materials. But there are still issues to be resolved, like protecting data privacy, managing human - AI interaction well, and preventing biases in information produced by AI. The process of creating a generative AI system for teaching includes gathering data, choosing a model, training it, and deploying it. Although scalable, personalized, and engaging learning solutions offered by generative AI hold great promise for revolutionizing education, there are a number of drawbacks that may restrict the technology's applicability and prevent it from being widely used. The difficulty of maintaining and upgrading these systems, ethical and privacy problems, and the caliber and bias of the produced information are examples of technical constraints. The applications, legal frameworks, and social consequences of generative AI will be shaped by its technological limits. To fully realize the benefits of AI in education, issues including data privacy breaches, possible bias in AI systems, and the digital divide must be resolved.