arXiv:2601.06500cs.AIcs.CY2026-01

构建AI能力框架,区分三类人才需求,助力社会应对智能时代挑战。

The AI Pyramid A Conceptual Framework for Workforce Capability in the Age of AI

  • 提出AI金字塔模型,分三层定义人类在AI时代的三大核心能力。
  • 强调高阶能力需系统性培养,而非零散培训,支持长期发展。
  • 适合组织、教育机构与政府参考,推动政策与人才培养协同。

人工智能正引发技术变革的质变,不仅自动化常规任务,更延伸认知劳动。最新证据表明,生成式AI对高学历白领影响尤为显著,挑战了传统对劳动力脆弱性的认知,也使传统数字或AI素养培训不再足够。本文提出‘AI原生性’概念——即把AI自然融入日常思维、问题解决与决策的能力,并构建‘AI金字塔’框架,将人类能力分为三个相互依赖的层级:普遍基础的AI原生能力,用于参与AI增强环境;构建、整合与维护AI系统的AI基础能力;以及推进前沿AI知识与应用的AI深度能力。该金字塔并非职业晋升路径,而是系统层面所需的规模化能力分布。文章主张,有效的人才发展应将能力建设视为基础设施,通过嵌入工作场景的问题导向学习,结合动态技能本体与能力导向评估实现。该框架对组织、教育体系和政府具有重要意义,有助于协调学习、评估与政策,以应对智能时代下的生产率提升、韧性建设与不平等问题。

原文摘要 · Abstract (English)

Artificial intelligence (AI) represents a qualitative shift in technological change by extending cognitive labor itself rather than merely automating routine tasks. Recent evidence shows that generative AI disproportionately affects highly educated, white collar work, challenging existing assumptions about workforce vulnerability and rendering traditional approaches to digital or AI literacy insufficient. This paper introduces the concept of AI Nativity, the capacity to integrate AI fluidly into everyday reasoning, problem solving, and decision making, and proposes the AI Pyramid, a conceptual framework for organizing human capability in an AI mediated economy. The framework distinguishes three interdependent capability layers: AI Native capability as a universal baseline for participation in AI augmented environments; AI Foundation capability for building, integrating, and sustaining AI enabled systems; and AI Deep capability for advancing frontier AI knowledge and applications. Crucially, the pyramid is not a career ladder but a system level distribution of capabilities required at scale. Building on this structure, the paper argues that effective AI workforce development requires treating capability formation as infrastructure rather than episodic training, centered on problem based learning embedded in work contexts and supported by dynamic skill ontologies and competency based measurement. The framework has implications for organizations, education systems, and governments seeking to align learning, measurement, and policy with the evolving demands of AI mediated work, while addressing productivity, resilience, and inequality at societal scale.

AI能力教育改革劳动力转型

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