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Co-Skilling an AI-Ready America: A Policy Framework for Bridging the AI Skills Gap across the U.S. Workforce

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Title: Co-Skilling an AI-Ready America: A Policy Framework for Bridging the AI Skills Gap across the U.S. Workforce
Language: English
Authors: Shubham Gupta
Source: Online Submission. 2026.
Peer Reviewed: N
Page Count: 17
Publication Date: 2026
Document Type: Reports - Descriptive
Education Level: Elementary Secondary Education
Higher Education
Postsecondary Education
Two Year Colleges
Adult Education
Descriptors: Artificial Intelligence, Job Skills, Technological Literacy, Soft Skills, Labor Force Development, Federal Legislation, Labor Legislation, Apprenticeships, Skill Development, Elementary Secondary Education, Community Colleges, Adult Education, Employers, Public Policy
Laws, Policies and Program Identifiers: Workforce Innovation and Opportunity Act 2014
Abstract: The emergence of generative artificial intelligence as a pervasive workplace technology has exposed a structural gap in the U.S. workforce development system: existing training architectures were designed for incremental skill change, not for the continuous, cross-occupational learning that AI-driven transformation demands. Although the World Economic Forum projects a net global gain of 78 million jobs by 2030, the distributional challenge is severe: 39% of core workforce skills will change or become obsolete within five years, yet only 31% of workers report receiving any AI-related training from their employers. This article proposes an AI-era competency and co-skilling framework grounded in the U.S. Department of Labor's 2026 AI Literacy Framework and aligned with the policy instruments available to states, workforce boards, community colleges, and employers. I define co-skilling as the simultaneous, collaborative development of AI technical competencies and the distinctly human skills (critical thinking, adaptability, ethical reasoning, and communication) that AI augments but cannot replace. I present a three-tier competency architecture, map institutional responsibilities across the delivery ecosystem, and offer specific policy recommendations for operationalizing co-skilling at scale through WIOA modernization, Workforce Pell, registered apprenticeship integration, and AI Workforce Centers of Excellence. The framework positions AI skill development not as a one-time credential event but as a sustained institutional practice embedded in work, credentialed through flexible pathways, and governed through shared accountability across education, workforce, and employer systems.
Abstractor: As Provided
Entry Date: 2026
Accession Number: ED680245
Database: ERIC
FullText Text:
  Availability: 0
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  Data: The emergence of generative artificial intelligence as a pervasive workplace technology has exposed a structural gap in the U.S. workforce development system: existing training architectures were designed for incremental skill change, not for the continuous, cross-occupational learning that AI-driven transformation demands. Although the World Economic Forum projects a net global gain of 78 million jobs by 2030, the distributional challenge is severe: 39% of core workforce skills will change or become obsolete within five years, yet only 31% of workers report receiving any AI-related training from their employers. This article proposes an AI-era competency and co-skilling framework grounded in the U.S. Department of Labor's 2026 AI Literacy Framework and aligned with the policy instruments available to states, workforce boards, community colleges, and employers. I define co-skilling as the simultaneous, collaborative development of AI technical competencies and the distinctly human skills (critical thinking, adaptability, ethical reasoning, and communication) that AI augments but cannot replace. I present a three-tier competency architecture, map institutional responsibilities across the delivery ecosystem, and offer specific policy recommendations for operationalizing co-skilling at scale through WIOA modernization, Workforce Pell, registered apprenticeship integration, and AI Workforce Centers of Excellence. The framework positions AI skill development not as a one-time credential event but as a sustained institutional practice embedded in work, credentialed through flexible pathways, and governed through shared accountability across education, workforce, and employer systems.
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      – Text: English
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        PageCount: 17
    Subjects:
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Job Skills
        Type: general
      – SubjectFull: Technological Literacy
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      – SubjectFull: Soft Skills
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      – SubjectFull: Federal Legislation
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      – SubjectFull: Labor Legislation
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      – SubjectFull: Apprenticeships
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      – SubjectFull: Elementary Secondary Education
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      – SubjectFull: Community Colleges
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      – SubjectFull: Workforce Innovation and Opportunity Act 2014
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      – TitleFull: Co-Skilling an AI-Ready America: A Policy Framework for Bridging the AI Skills Gap across the U.S. Workforce
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              Y: 2026
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