arXiv:2602.08295cs.AI2026-02被引 1

GenAI让编程教育从写规则转向把握语境,重塑人机协作新范式。

The Vibe-Automation of Automation: A Proactive Education Framework for Computer Science in the Age of Generative AI

  • 用'氛围自动化'描述GenAI在语义与情境中捕捉隐性规律的能力
  • 人类角色从写算法变为调控系统上下文与对齐关系的'氛围工程'
  • 适合高校教育者、课程设计者及科技政策制定者思考未来教学变革

生成式人工智能(GenAI)带来的不仅是技术进步,更是一种认知层面的根本转变,挑战了计算机科学的基础假设。如果说机器学习是‘自动化的自动化’,那么GenAI则通过理解上下文、语义和风格一致性来运作,而非优化预设目标函数。本文提出‘氛围自动化’(Vibe-Automation)概念,强调其核心在于对实践中的隐性规律——即无法完全用显式规则表达的上下文敏感模式——的功能性访问。尽管生成模型并无现象学意义上的隐性知识,但它们在高维潜在表示中编码了对语气、意图和情境判断的敏感性。在此基础上,人类角色从算法问题定义转向‘氛围工程’,即对齐与情境判断的协调。论文进一步构建了一个跨三层次(观念、产业、课程)的主动教育框架,讨论模式坍缩与文化同质化风险,强调需主动介入以避免陷入合成一致性的退化。

原文摘要 · Abstract (English)

The emergence of generative artificial intelligence (GenAI) represents not an incremental technological advance but a qualitative epistemological shift that challenges foundational assumptions of computer science. Whereas machine learning has been described as the automation of automation, generative AI operates by navigating contextual, semantic, and stylistic coherence rather than optimizing predefined objective metrics. This paper introduces the concept of Vibe-Automation to characterize this transition. The central claim is that the significance of GenAI lies in its functional access to operationalized tacit regularities: context-sensitive patterns embedded in practice that cannot be fully specified through explicit algorithmic rules. Although generative systems do not possess tacit knowledge in a phenomenological sense, they operationalize sensitivities to tone, intent, and situated judgment encoded in high-dimensional latent representations. On this basis, the human role shifts from algorithmic problem specification toward Vibe-Engineering, understood as the orchestration of alignment and contextual judgment in generative systems. The paper connects this epistemological shift to educational and institutional transformation by proposing a conceptual framework structured across three analytical levels and three domains of action: faculty worldview, industry relations, and curriculum design. The risks of mode collapse and cultural homogenization are briefly discussed, emphasizing the need for deliberate engagement with generative systems to avoid regression toward synthetic uniformity.

生成式AI教育变革人机协同

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