In the era of artificial intelligence (AI), Large Language Models (LLMs) have become increasingly significant in various domains. This paper discusses the necessity and urgency for software engineering education to adapt and evolve to prepare engineers for the emerging LLM environment. We explore several critical areas, including understanding LLMs and AI principles, ethics and responsible AI, data science, debugging and understanding AI model outputs, adaptability, model training and tuning, integration and application, law and regulation, and pedagogy. With the objective of providing a comprehensive overview of the needs in software engineering education, we argue that a holistic approach is needed, one that combines technical skills, ethical awareness, legal knowledge, and adaptable learning strategies. This paper aims to contribute to the ongoing conversation about the future of software engineering education, emphasizing the importance of adapting and evolving to remain in sync with rapid advancements in AI and LLMs. We hope this exploration provides valuable insights for educators, curriculum developers, and policymakers in software engineering.