English

AI-accelerated End-to-End Framework for Rapid Professional Upskilling

Artificial Intelligence 2026-07-15 v1

Abstract

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.

Cite

@article{arxiv.2607.14044,
  title  = {AI-accelerated End-to-End Framework for Rapid Professional Upskilling},
  author = {Tam Nguyen and Hung Nguyen and Robert Ogburn},
  journal= {arXiv preprint arXiv:2607.14044},
  year   = {2026}
}

Comments

6 pages, 1 figure, 1 table