AI 中文总结
研究旨在解决工人技能提升时间长且现有框架缺乏行业验证的问题，提出人工智能加速的端到端框架，涵盖知识获取等五个阶段，通过三个外部信号验证，如获专业机构认可、学习者快速通过考试及支持复杂分析。
AI 中文摘要
到2030年，每100名工人中就有59人需要重新学习或提升技能，但企业弥合技能差距的平均时间从2014年的约3天增长到了2018年的36天。当前大多数框架仅加速技能提升计划的单个阶段，且普遍缺乏行业验证。我们提出了一个端到端框架，该框架在知识获取、内容开发、内容审查与验证、教学以及评估开发这五个阶段应用人工智能加速，同时高度关注生产和学习效率。有三个有力的外部信号验证了该框架：美国国家会计委员会审查并批准了基于该框架的持续专业教育学分提升计划；3名学习者按照该计划在极短时间内通过了NVIDIA认证的智能体人工智能专业考试，另有14人正在学习；该计划的知识库支持复杂的下游分析，比如生成用于管理多智能体人工智能系统风险的包含1267个风险项目的强大数据集。
英文摘要
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 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.
Comments6 pages, 1 figure, 1 table
