Generative AI represents a turning point for Computer Science (CS) education. In recent decades, post-secondary CS education has largely focused on what has been seen as practical software engineering skills: implementation-level programming, debugging, testing, and software design, analysis, and documentation. However, this framing is becoming less tenable as generative AI automates many of these tasks, challenging their centrality in CS education. To keep pace with advances in AI technology, CS curricula should consider a shift toward understanding and verifying AI-generated artifacts. This white paper outlines the findings of two international NUS-Google Workshops in Singapore, where we convened faculty members, industry practitioners, and students, and proposes a strategic response to reshape how CS should be taught at the undergraduate level. Based on the findings, we identify critical skills that must be preserved and those that are becoming less important. By incorporating these skills as "breadcrumbs," we can provide helpful nudges and engaging exercises within the current curriculum, enhancing learning experiences for everyone. We believe that to effectively prepare future computer science graduates, capable of creating, solving problems, and managing, as well as co-creating, artifacts with AI. It is important to consider a shift in curricula. Emphasizing system design, abstraction, and critical evaluation could greatly enhance their education and readiness for the challenges ahead. We propose prerequisites for solutions to reform CS education by fostering AI-native competencies, re-centering fundamental education, enhancing advanced pathways, embracing new pedagogies, and shifting institutional support.

翻译：生成式AI代表了计算机科学（CS）教育的转折点。近几十年来，高等教育阶段的CS教育主要聚焦于被视为实用软件工程技能的领域：实现层面的编程、调试、测试，以及软件设计、分析与文档编写。然而，随着生成式AI自动化诸多此类任务，这一框架的合理性日益受到挑战，动摇了其在CS教育中的核心地位。为跟上AI技术的进步，CS课程应考虑转向理解与验证AI生成的产物。本白皮书概述了在新加坡举行的两次国际NUS-Google研讨会的发现，我们召集了教师、行业从业者与学生，并据此提出了一项战略响应，以重塑本科阶段CS教学方式。基于这些发现，我们确定了必须保留的关键技能以及重要性逐渐下降的技能。通过将此类技能作为“面包屑”融入现有课程，我们可提供有益的提示与互动练习，从而提升所有学习者的体验。我们认为，为有效培养未来能够创建、解决问题、管理并与AI共同创造产物的计算机科学毕业生，考虑课程转向至关重要。强调系统设计、抽象与批判性评估，将大大增强其教育效果，并为应对未来挑战做好准备。我们提出了改革CS教育的解决方案所需的前提条件，包括培养AI原生能力、重归基础核心教育、强化高阶路径、采纳新教学法，以及转变机构支持模式。