AI时代提升智能制造劳动力准备度的概念框架
A Conceptual Framework for Enhancing Workforce Readiness for Smart Manufacturing in the AI Era
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中文总结 AI 辅助
针对AI时代智能制造劳动力能力缺口，本文提出含九个阶段、四个支柱的WRL框架，经89个顶点项目验证可诊断能力差距，为教育等领域提供循证工具，未来将校准支柱权重并测试信效度。
中文摘要 AI 辅助
人工智能（AI）、工业物联网、信息物理系统与先进机器人技术的融合正以工程课程难以适配的速度重塑制造业，扩大了车间所需能力与传统工程及技术教育所提供能力之间的差距。本文提出劳动力准备度等级（WRL）框架，该框架将技术准备度等级量表调整为九个递进的能力阶段，以及四个支柱维度：数字与AI素养、信息物理系统熟练度、人机协作、数据驱动决策，在“无薄弱支柱”规则下通过综合阶段得分和队列级劳动力准备度指数进行汇总。该框架在一所大学的智能制造教学实验室中得到实例化，依托四个学期内完成的89个赞助顶点项目，其中四个被深入分析。四个支柱共同覆盖了相关的ABET学生成果。在重点队列中，劳动力准备度指数介于5.2至6.4之间，“无薄弱支柱”规则在四个案例中的三个具有诊断参考价值，在一个案例中为约束性认证条件，多次揭示了被强劲分析能力表象掩盖的信息物理系统与数据驱动决策方面的差距；向最高阶段的推进由行业嵌入经验而非额外课程作业决定。WRL为教育者、认证机构和区域劳动力系统提供了一种通用的、循证的工具，用于诊断和提升劳动力准备度；未来工作将校准支柱权重并测试其可靠性和预测效度。
英文摘要
The convergence of artificial intelligence (AI), Industrial Internet of Things, cyber-physical systems, and advanced robotics is reshaping manufacturing faster than engineering curricula can adapt, widening the gap between the competencies required on the shop floor and those delivered by traditional engineering and technology education. This paper proposes a Workforce Readiness Level (WRL) framework, which adapts the Technology Readiness Level scale into nine progressive competency stages and a four-pillar rubric, digital and AI literacy, cyber-physical systems fluency, human-machine collaboration, and data-driven decision making, aggregated through a composite stage score and a cohort-level workforce-readiness index under a ``no-thin-pillar'' rule. The framework is instantiated at a university smart-manufacturing teaching laboratory and draws on 89 sponsored capstone projects delivered over four semesters, four of which are analyzed in depth. Four pillars jointly span the relevant ABET student outcomes. Across the highlighted cohorts the workforce-readiness index ranged from 5.2 to 6.4, and the no-thin-pillar rule was diagnostically informative in three of the four cases and the binding certification constraint in one, repeatedly surfacing cyber-physical and data-driven-decision gaps concealed behind strong analytics profiles; advancement to the highest stages was gated by industry-embedded experience rather than additional coursework. WRL offers educators, accreditation bodies, and regional workforce systems a common, evidence-based instrument for diagnosing and advancing workforce readiness; future work will calibrate pillar weights and test reliability and predictive validity.
发表机构
- Mississippi State University（密西西比州立大学）
机构由 AI 辅助整理，请以论文原文为准。
