用AI全流程提速专业技能提升，36天缩短至数天。
AI-accelerated End-to-End Framework for Rapid Professional Upskilling
- AI贯穿五个阶段：知识获取到评估开发，全程自动化加速。
- 3人快速通过NVIDIA认证考试，14人正在学习中。
- 可生成1267项风险数据集，适合企业与AI安全研究者。
到2030年，每100名员工中有59人需要重新培训或技能提升，但企业技能缺口的平均解决时间已从2014年的约3天增至2018年的36天。现有框架多仅加速单一环节，且缺乏行业验证。本文提出一个端到端框架，利用AI在知识获取、内容开发、审核验证、教学实施和评估设计五个阶段实现加速，兼顾生产与学习效率。三项外部验证支持该框架：美国州会计委员会批准基于该框架的培训项目获取持续专业教育学分；3名学习者在极短时间内通过NVIDIA认证的智能体AI专业人员考试，另有14人正在进行；其知识库支持复杂下游分析，如生成1,267项风险条目，用于管理多智能体系统风险。
原文摘要 · Abstract (English)
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 followed the program and passed the NVIDIA Certified Professional in Agentic AI exam in a significantly short amount of time, with 14 more in progress; the program's knowledge base supports complex downstream analysis such as the production of a robust 1,267 risk item dataset for managing multi-agent AI system risks.
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