Enhancing U.S. K-12 Competitiveness for the Agentic Generative AI Era: A Structured Framework for Educators and Policy Makers
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| Title: | Enhancing U.S. K-12 Competitiveness for the Agentic Generative AI Era: A Structured Framework for Educators and Policy Makers |
|---|---|
| Language: | English |
| Authors: | Satyadhar Joshi (ORCID |
| Source: | Online Submission. 2025. |
| Peer Reviewed: | N |
| Page Count: | 26 |
| Publication Date: | 2025 |
| Intended Audience: | Practitioners; Policymakers |
| Document Type: | Information Analyses |
| Education Level: | Elementary Secondary Education |
| Descriptors: | Elementary Secondary Education, Artificial Intelligence, Technology Integration, Federal Legislation, Educational Legislation, Educational Change, Curriculum Development, Faculty Development, Technological Literacy, Curriculum Implementation, Foreign Countries, Comparative Analysis, Cross Cultural Studies, Global Approach |
| Geographic Terms: | United States, Finland, China, United Kingdom, Germany |
| Abstract: | This paper presents a comprehensive framework for transforming K-12 education through systematic AI integration, addressing critical gaps in curriculum development and teacher preparedness. Drawing from extensive analysis of federal initiatives, including the 2025 White House Executive Order on advancing AI education, and synthesizing insights from recent scholarly and policy sources, we propose a multi-tiered approach to educational reform. This paper presents a strategic framework for transforming U.S. K-12 education through AI-integrated curriculum development and professional development programs. Our research reveals significant disparities in current implementation, with only 20-25% of educators feeling adequately prepared for AI integration despite 60-70% recognizing its importance. The framework encompasses AI literacy competencies across grade levels, differentiated professional development pathways, and a detailed technical architecture for generative AI tools in educational settings. We provide empirical evidence from international benchmarks, demonstrating that systematic approaches like Finland's "Generation AI" project achieve 80-90% teacher participation rates compared to 30-40% in U.S. programs. The proposed model includes phased implementation strategies, resource allocation frameworks totaling $7.2 million over three years, and comprehensive assessment mechanisms. Our findings indicate that schools implementing structured AI curricula report 25-35% higher student STEM engagement and 40-50% gains in computational thinking scores. The paper addresses critical ethical considerations, equity implications, and policy recommendations to guide sustainable AI integration while maintaining human-centered educational values. The proposed model aligns with national priorities for maintaining U.S. competitiveness in global AI education landscapes while ensuring equitable access and responsible AI implementation across diverse educational contexts. All results, projections, proposals are from cited literature. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | ED676035 |
| Database: | ERIC |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=ED676035 Name: ERIC Full Text Category: fullText Text: Full Text from ERIC |
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| Items | – Name: Title Label: Title Group: Ti Data: Enhancing U.S. K-12 Competitiveness for the Agentic Generative AI Era: A Structured Framework for Educators and Policy Makers – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Satyadhar+Joshi%22">Satyadhar Joshi</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0002-6011-5080">0009-0002-6011-5080</externalLink>) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="SO" term="%22Online+Submission%22"><i>Online Submission</i></searchLink>. 2025. – Name: PeerReviewed Label: Peer Reviewed Group: SrcInfo Data: N – Name: Pages Label: Page Count Group: Src Data: 26 – Name: DatePubCY Label: Publication Date Group: Date Data: 2025 – Name: Audience Label: Intended Audience Group: Audnce Data: Practitioners; Policymakers – Name: TypeDocument Label: Document Type Group: TypDoc Data: Information Analyses – Name: Audience Label: Education Level Group: Audnce Data: <searchLink fieldCode="EL" term="%22Elementary+Secondary+Education%22">Elementary Secondary Education</searchLink> – Name: Subject Label: Descriptors Group: Su Data: <searchLink fieldCode="DE" term="%22Elementary+Secondary+Education%22">Elementary Secondary Education</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Integration%22">Technology Integration</searchLink><br /><searchLink fieldCode="DE" term="%22Federal+Legislation%22">Federal Legislation</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Legislation%22">Educational Legislation</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Change%22">Educational Change</searchLink><br /><searchLink fieldCode="DE" term="%22Curriculum+Development%22">Curriculum Development</searchLink><br /><searchLink fieldCode="DE" term="%22Faculty+Development%22">Faculty Development</searchLink><br /><searchLink fieldCode="DE" term="%22Technological+Literacy%22">Technological Literacy</searchLink><br /><searchLink fieldCode="DE" term="%22Curriculum+Implementation%22">Curriculum Implementation</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Comparative+Analysis%22">Comparative Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Cross+Cultural+Studies%22">Cross Cultural Studies</searchLink><br /><searchLink fieldCode="DE" term="%22Global+Approach%22">Global Approach</searchLink> – Name: Subject Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22United+States%22">United States</searchLink><br /><searchLink fieldCode="DE" term="%22Finland%22">Finland</searchLink><br /><searchLink fieldCode="DE" term="%22China%22">China</searchLink><br /><searchLink fieldCode="DE" term="%22United+Kingdom%22">United Kingdom</searchLink><br /><searchLink fieldCode="DE" term="%22Germany%22">Germany</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper presents a comprehensive framework for transforming K-12 education through systematic AI integration, addressing critical gaps in curriculum development and teacher preparedness. Drawing from extensive analysis of federal initiatives, including the 2025 White House Executive Order on advancing AI education, and synthesizing insights from recent scholarly and policy sources, we propose a multi-tiered approach to educational reform. This paper presents a strategic framework for transforming U.S. K-12 education through AI-integrated curriculum development and professional development programs. Our research reveals significant disparities in current implementation, with only 20-25% of educators feeling adequately prepared for AI integration despite 60-70% recognizing its importance. The framework encompasses AI literacy competencies across grade levels, differentiated professional development pathways, and a detailed technical architecture for generative AI tools in educational settings. We provide empirical evidence from international benchmarks, demonstrating that systematic approaches like Finland's "Generation AI" project achieve 80-90% teacher participation rates compared to 30-40% in U.S. programs. The proposed model includes phased implementation strategies, resource allocation frameworks totaling $7.2 million over three years, and comprehensive assessment mechanisms. Our findings indicate that schools implementing structured AI curricula report 25-35% higher student STEM engagement and 40-50% gains in computational thinking scores. The paper addresses critical ethical considerations, equity implications, and policy recommendations to guide sustainable AI integration while maintaining human-centered educational values. The proposed model aligns with national priorities for maintaining U.S. competitiveness in global AI education landscapes while ensuring equitable access and responsible AI implementation across diverse educational contexts. All results, projections, proposals are from cited literature. – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: DateEntry Label: Entry Date Group: Date Data: 2025 – Name: AN Label: Accession Number Group: ID Data: ED676035 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=ED676035 |
| RecordInfo | BibRecord: BibEntity: Languages: – Text: English PhysicalDescription: Pagination: PageCount: 26 Subjects: – SubjectFull: Elementary Secondary Education Type: general – SubjectFull: Artificial Intelligence Type: general – SubjectFull: Technology Integration Type: general – SubjectFull: Federal Legislation Type: general – SubjectFull: Educational Legislation Type: general – SubjectFull: Educational Change Type: general – SubjectFull: Curriculum Development Type: general – SubjectFull: Faculty Development Type: general – SubjectFull: Technological Literacy Type: general – SubjectFull: Curriculum Implementation Type: general – SubjectFull: Foreign Countries Type: general – SubjectFull: Comparative Analysis Type: general – SubjectFull: Cross Cultural Studies Type: general – SubjectFull: Global Approach Type: general – SubjectFull: United States Type: general – SubjectFull: Finland Type: general – SubjectFull: China Type: general – SubjectFull: United Kingdom Type: general – SubjectFull: Germany Type: general Titles: – TitleFull: Enhancing U.S. K-12 Competitiveness for the Agentic Generative AI Era: A Structured Framework for Educators and Policy Makers Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Satyadhar Joshi IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Type: published Y: 2025 Titles: – TitleFull: Online Submission Type: main |
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