Abstract & Details
Description
Award ID: 2627658
This FINDERS Foundry award supports an AI-enhanced career-readiness framework aligned with K-12 workforce development, interdisciplinary innovation, and technology-enabled experiential learning. Workforce research consistently shows that employers prioritize soft skills such as communication, collaboration, problem solving, leadership, and adaptability as essential to employment success. However, students often lack access to authentic learning experiences that develop these competencies. This project addresses that gap by producing a scalable, standards-aligned framework that can be adapted across educational contexts. The adaptable design supports expansion of high-quality career preparation pathways. This FINDERS Foundry award addresses a foundational gap in career readiness research: the absence of scalable models that integrate AI-supported learning, immersive simulation technologies, and competency-based assessment in K12 education. The interdisciplinary leadership team of educators, caregivers, technologists and researchers will co-design an AI-enhanced immersive career readiness framework, develop prototype learning experiences, assess technological and implementation feasibility, and establish measurable student outcomes and evaluation strategies. These outcomes establish the research infrastructure needed to evaluate whether AI-supported immersive simulations can serve as effective experiential learning environments for preparing high school students for evolving workforce expectations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
NSF Program Director: Lindsay Portnoy
This FINDERS Foundry award supports an AI-enhanced career-readiness framework aligned with K-12 workforce development, interdisciplinary innovation, and technology-enabled experiential learning. Workforce research consistently shows that employers prioritize soft skills such as communication, collaboration, problem solving, leadership, and adaptability as essential to employment success. However, students often lack access to authentic learning experiences that develop these competencies. This project addresses that gap by producing a scalable, standards-aligned framework that can be adapted across educational contexts. The adaptable design supports expansion of high-quality career preparation pathways. This FINDERS Foundry award addresses a foundational gap in career readiness research: the absence of scalable models that integrate AI-supported learning, immersive simulation technologies, and competency-based assessment in K12 education. The interdisciplinary leadership team of educators, caregivers, technologists and researchers will co-design an AI-enhanced immersive career readiness framework, develop prototype learning experiences, assess technological and implementation feasibility, and establish measurable student outcomes and evaluation strategies. These outcomes establish the research infrastructure needed to evaluate whether AI-supported immersive simulations can serve as effective experiential learning environments for preparing high school students for evolving workforce expectations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
NSF Program Director: Lindsay Portnoy
| Status | Active |
|---|---|
| Effective start/end date | 07/15/26 → 10/31/26 |
Active Fiscal Year
- FY2027
- FY2026
Start Fiscal Year
- FY2026
TIP Programs
- (FF) FINDERS FOUNDRY
Key Technology Areas
- Artificial Intelligence
- (confidence score: 99%)
Technology Foci
- Artificial Intelligence (excluding ML)
- (confidence score: 87%)
Congressional District at Award
- District n. 03 of Connecticut
Current Congressional District
- District n. 03 of Connecticut
United States
- Connecticut
Core Based Statistical Area (CBSA)
- New Haven, CT
County
- County: South Central Connecticut, CT
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