Large language models (LLMs) are transforming education by enabling new possibilities in student and teacher assistance, adaptive learning, and commercial tools. This survey provides a comprehensive review of LLM applications in education, covering various technologies, datasets, and benchmarks. It identifies risks and challenges associated with LLM deployment in education, such as bias, overreliance, and inequitable access. The survey also outlines future research opportunities, emphasizing the potential for innovation in education through LLMs. LLMs are being used to assist students with question solving, error correction, and confusion resolution. They can generate high-quality answers, correct grammar and spelling errors, and provide guidance for problem-solving. In teaching, LLMs assist educators with question generation, automatic grading, and material creation. They can generate interactive learning materials, automate grading, and streamline lesson planning. LLMs also support adaptive learning by tracking student progress and personalizing content based on individual needs. LLMs are being integrated into educational tools such as chatbots, content creation platforms, and quiz generators. These tools offer personalized feedback, interactive learning experiences, and efficient assessment methods. However, the use of LLMs in education raises concerns about fairness, reliability, transparency, privacy, and overreliance. Efforts are being made to address these issues through responsible AI frameworks, bias mitigation strategies, and ethical considerations. Future directions for LLMs in education include pedagogically aligned models, multi-agent systems, multimodal and multilingual support, edge computing, and efficient training of specialized models. These advancements aim to enhance personalized learning, improve educational equity, and ensure the responsible use of LLMs in education. The survey concludes that LLMs have the potential to revolutionize education by providing more effective, personalized, and accessible learning experiences.Large language models (LLMs) are transforming education by enabling new possibilities in student and teacher assistance, adaptive learning, and commercial tools. This survey provides a comprehensive review of LLM applications in education, covering various technologies, datasets, and benchmarks. It identifies risks and challenges associated with LLM deployment in education, such as bias, overreliance, and inequitable access. The survey also outlines future research opportunities, emphasizing the potential for innovation in education through LLMs. LLMs are being used to assist students with question solving, error correction, and confusion resolution. They can generate high-quality answers, correct grammar and spelling errors, and provide guidance for problem-solving. In teaching, LLMs assist educators with question generation, automatic grading, and material creation. They can generate interactive learning materials, automate grading, and streamline lesson planning. LLMs also support adaptive learning by tracking student progress and personalizing content based on individual needs. LLMs are being integrated into educational tools such as chatbots, content creation platforms, and quiz generators. These tools offer personalized feedback, interactive learning experiences, and efficient assessment methods. However, the use of LLMs in education raises concerns about fairness, reliability, transparency, privacy, and overreliance. Efforts are being made to address these issues through responsible AI frameworks, bias mitigation strategies, and ethical considerations. Future directions for LLMs in education include pedagogically aligned models, multi-agent systems, multimodal and multilingual support, edge computing, and efficient training of specialized models. These advancements aim to enhance personalized learning, improve educational equity, and ensure the responsible use of LLMs in education. The survey concludes that LLMs have the potential to revolutionize education by providing more effective, personalized, and accessible learning experiences.