Generative Artificial Intelligence (GenAI), Large Language Models (LLMs), and emerging Agentic AI constitute the most disruptive transformation in the history of software engineering (SE), reshaping development processes, required competencies, professional roles, and the educational outcomes that universities must deliver. This paper presents a systematic review of 48 verified, influential peer-reviewed publications (2016--2026) drawn from leading venues in software engineering, machine learning, computing education, human--AI collaboration, and software productivity. Studies were discovered, screened, and analyzed through a four-agent research workflow (Literature Discovery, Scientometric Analysis, Curriculum Transformation, and Workforce Impact) and were verified against primary sources. We synthesize the evidence along nine themes and three trajectories -- practice, education, and workforce -- and report a scientometric inflection in which annual LLM-for-SE output grew roughly five-fold after late 2022. From this synthesis we contribute: (i) a conceptual framework for AI-native software engineering organized around \emph{intent}, \emph{collaboration}, and \emph{verification}; (ii) a nine-dimension competency model spanning specification, critical evaluation, agent orchestration, and metacognition; (iii) a four-phase university curriculum roadmap with AI-resilient assessment; (iv) faculty-development and workforce-transformation strategies; and (v) a prioritized agenda of eleven research gaps. The evidence base is internally contradictory on the magnitude and direction of productivity effects, underscoring that benefits are strongly context-dependent and that educating engineers for judgment, verification, and orchestration -- rather than code production alone -- is the central challenge of the AI-native era.

翻译：生成式人工智能（GenAI）、大型语言模型（LLM）及新兴的自主智能体系统构成了软件工程历史上最具颠覆性的变革，重塑了开发流程、所需能力、专业角色，以及大学必须培养的教育成果。本文对2016至2026年间来自软件工程、机器学习、计算教育、人机协作及软件生产力领域48篇经核验、具有影响力的同行评审出版物进行了系统性综述。研究通过一个四智能体研究工作流（文献发现、科学计量分析、课程转型与劳动力影响）进行发现、筛选与分析，并与原始来源逐一核验。我们沿九个主题及三条轨迹（实践、教育及劳动力）综合证据，并报告了一个科学计量转折点：2022年底后，LLM用于软件工程的年产出增长约五倍。基于此综合，我们贡献：（i）围绕意图、协作与验证组织的AI原生软件工程概念框架；（ii）涵盖规格说明、批判性评估、智能体编排及元认知的九维能力模型；（iii）包含AI韧性评估方法的四阶段大学课程路线图；（iv）师资发展与劳动力转型策略；及（v）包含11项研究空白的优先议程。证据库在生产力效应的幅度与方向上存在内部矛盾，突显了收益高度依赖于具体情境，而培养工程师的判断力、验证与编排能力——而非仅代码生成——是AI原生时代面临的核心挑战。