The paper discusses the need for software engineering education to adapt and evolve in response to the rise of Large Language Models (LLMs) and generative AI. As LLMs become more prevalent in software development, they present both opportunities and challenges for the field. The paper argues that software engineering education must incorporate a holistic understanding of LLMs, including their technical capabilities, ethical implications, and practical applications. It emphasizes the importance of developing students' technical skills, ethical awareness, and adaptability in a rapidly changing field. The paper explores how software engineering education should change to reflect the new realities of LLMs, including updates to course content, pedagogy, and curriculum design. It also highlights the need for ethical considerations in AI development, including issues such as bias, transparency, privacy, and intellectual property. The paper suggests that software engineering curricula should include interdisciplinary perspectives and collaborative skills development, as LLMs can enable new applications and domains that require knowledge from different fields. It also emphasizes the importance of critical and reflective thinking in software engineering education, as LLMs can generate incorrect or biased outputs that may have negative consequences. The paper concludes that software engineering education must evolve to better prepare students for the challenges and opportunities presented by LLMs and generative AI.The paper discusses the need for software engineering education to adapt and evolve in response to the rise of Large Language Models (LLMs) and generative AI. As LLMs become more prevalent in software development, they present both opportunities and challenges for the field. The paper argues that software engineering education must incorporate a holistic understanding of LLMs, including their technical capabilities, ethical implications, and practical applications. It emphasizes the importance of developing students' technical skills, ethical awareness, and adaptability in a rapidly changing field. The paper explores how software engineering education should change to reflect the new realities of LLMs, including updates to course content, pedagogy, and curriculum design. It also highlights the need for ethical considerations in AI development, including issues such as bias, transparency, privacy, and intellectual property. The paper suggests that software engineering curricula should include interdisciplinary perspectives and collaborative skills development, as LLMs can enable new applications and domains that require knowledge from different fields. It also emphasizes the importance of critical and reflective thinking in software engineering education, as LLMs can generate incorrect or biased outputs that may have negative consequences. The paper concludes that software engineering education must evolve to better prepare students for the challenges and opportunities presented by LLMs and generative AI.