AI-accelerated End-to-End Framework for Rapid Professional Upskilling
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 41 references that are in the map. First posted on arXiv 15 Jul 2026.
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24 of 41 references.
References · 24
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- 2025Training LLM-based Tutors to Improve Student Learning Outcomes in Dialoguescited by 43
- 2024MCQG-SRefine: Multiple Choice Question Generation and Evaluation with Iterative Self-Critique, Correction, and Comparison Feedbackcited by 14
- 2025TutorGym: A Testbed for Evaluating AI Agents as Tutors and Studentscited by 10
- 2025Quantifying Cognitive Bias Induction in LLM-Generated Contentcited by 7
- 2025Assessing the Quality of AI-Generated Exams: A Large-Scale Field Studycited by 6
- 2026Some Simple Economics of AGIcited by 5
- 2025PlanGlow: Personalized Study Planning with an Explainable and Controllable LLM-Driven Systemcited by 3
- 2025Instructional Agents: Reducing Teaching Faculty Workload through Multi-Agent Instructional Designcited by 3
12 more
- 2026Brief but Impactful: How Human Tutoring Interactions Shape Engagement in Online Learningcited by 3
- 2025A systematic review of research on large language models for computer programming educationcited by 2
- 2025The Iceberg Index: Measuring Skills-centered Exposure in the AI Economycited by 2
- 2026Modernizing Ground Truth: Four Shifts Toward Improving Reliability and Validity in AI in Educationcited by 2
- 2025AI Technicians: Developing Rapid Occupational Training Methods for a Competitive AI Workforcecited by 1
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- 2025RAG-PRISM: A Personalized, Rapid, and Immersive Skill Mastery Framework with Adaptive Retrieval-Augmented Tutoringcited by 1
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- 2026A Classroom Study of LLM-Generated Feedback Intervention in Introductory Programmingcited by 1
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