[论文] AI-accelerated End-to-End Framework for Rapid Professional Upskilling

论文概要

研究领域: ML
作者: Tam Nguyen, Hung Nguyen, Robert Ogburn
发布时间: 2026-07-15
arXiv: 2607.14044

中文摘要

到2030年，每100名工人中将有59名需要再培训或技能提升，但关闭企业技能差距的平均时间从2014年的约3天增长到2018年的36天。大多数当前框架仅加速技能提升计划的单个阶段，且通常缺乏行业验证。我们提出一个端到端框架，在知识获取、内容开发、内容审查验证、教学和评估开发五个阶段应用AI加速；并强烈关注生产和学习效率。三个强有力的外部信号验证该框架：美国国家会计委员会协会审查并批准基于该框架的技能提升计划用于继续专业教育学分；3名学习者遵循该计划并在显著短时间内通过NVIDIA认证专业Agentic AI考试，另有14名正在进行中；该计划的知识库支持复杂下游分析，如生产用于管理多代理AI系统风险的1,267项风险数据集。

原文摘要

By 2030, 59 of every 100 workers will need reskilling or upskilling, yet the average time to close an enterprise skills gap grew from roughly 3 days in 2014 to 36 days in 2018. Most current frameworks accelerate single stages of upskilling programs and generally lack industry validation. We present an end-to-end framework that applies AI acceleration across five stages of knowledge acquisition, content development, content review and verification, teaching, and assessment development; with a strong focus on both production and learning efficiency. Three strong external signals validates the framework: the US National Association of State Boards of Accountancy reviewed and approved an upskilling program built on the framework for continuing-professional-education credits; 3 learners followe...

自动采集于 2026-07-17

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