Policy Recommendations for New Jersey's Artificial Intelligence Leadership in K-12, Higher Education, and Workforce Development
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| Title: | Policy Recommendations for New Jersey's Artificial Intelligence Leadership in K-12, Higher Education, and Workforce Development |
|---|---|
| Language: | English |
| Authors: | Satyadhar Joshi (ORCID |
| Source: | Online Submission. 2026. |
| Peer Reviewed: | N |
| Page Count: | 19 |
| Publication Date: | 2026 |
| Document Type: | Reports - Evaluative |
| Education Level: | Elementary Secondary Education Higher Education Postsecondary Education |
| Descriptors: | Artificial Intelligence, Elementary Secondary Education, Higher Education, Labor Force Development, Educational Policy, Governance, Adoption (Ideas), Ethics, Technology Integration, Incentives |
| Geographic Terms: | New Jersey |
| Abstract: | This paper presents a policy framework to position New Jersey as a national leader in artificial intelligence (AI) education and workforce development. Through analysis of current state initiatives--including the NJ AI Hub, AI Task Force reports, apprenticeship programs, and regulatory guidance--we identify gaps and opportunities across K-12, higher education, and workforce development sectors. We propose a multi-layered approach visualized through interconnected frameworks: an integrated AI education ecosystem, phased implementation roadmaps for K-12 AI literacy, a statewide AI curriculum consortium structure, multi-track workforce development pathways, and equity and access frameworks. Quantitative analysis reveals that while 20-25%+ of New Jersey's workforce already uses AI technology daily, only 20-25% of educators feel prepared for AI integration. Our policy recommendations address this gap through a $165 million annual investment strategy with projected 3.8x return on investment, creating pathways for 15,000-20,000 new AI jobs by 2030. Recommendations include more layered, interconnected and framework-styled methods for establishing AI literacy standards for all K-12 students, creating specialized AI high schools, expanding community college AI programs, developing industry-aligned university curricula, and implementing statewide AI teacher training. We also address equity and risk considerations, funding mechanisms, and suggested implementation timelines. This is a pure review paper and all findings are from suggested literature. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | ED678146 |
| Database: | ERIC |
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