The Rise of AI-Native Software Engineering: Implications for Practice, Education, and the Future Workforce

• URL: http://arxiv.org/abs/2606.12986v1
• Date: Thu, 11 Jun 2026 07:23:49 GMT
• Title: The Rise of AI-Native Software Engineering: Implications for Practice, Education, and the Future Workforce
• Abstract summary: Generative Artificial Intelligence (GenAI), Large Language Models (LLMs), and emerging Agentic AI constitute the most disruptive transformation in the history of software engineering.<n>This paper presents a systematic review of 48 verified, influential peer-reviewed publications.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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