2507751
Project Grant
Overview
Grant Description
SBIR Phase I: An adaptive AI-driven career exploration platform
The broader/commercial impact of this SBIR Phase I project is to ensure all Americans have access to 21st century careers while addressing a gap in workforce development through an adaptive, AI-driven career exploration platform which provides personalized career guidance for students in secondary and post-secondary education.
This unique approach to career readiness will reduce unemployment and skill mismatches across the nation by addressing critical stages in the career lifecycle of awareness, interest, and readiness.
By enhancing the technological infrastructure supporting career development and fostering a more adaptive, skilled, and competitive workforce, the project aligns with national interests towards accelerated access to meaningful career choices and lower rates of unemployment in critical areas.
The market opportunity for this project focuses on educational institutions and workforce development agencies, with the first market segment being high schools and post-secondary institutions.
By year three of deployment, this solution is projected to improve career readiness for thousands of individuals, improving labor market alignment, which will create significant economic value by creating a more engaged and aligned workforce.
This project will advance scientific understanding by developing and applying advanced learning theories and data-driven models to support open-field decision-making processes, fostering better career outcomes for all.
This Small Business Innovation Research (SBIR) Phase I project focuses on developing a data-driven platform to address the challenge of career discovery and decision-making in complex and evolving job markets.
The project integrates advanced learning theories to design state-of-the-art artificial intelligence and data visualization technologies to design and validate a novel system that contextualizes large datasets inclusive of such as labor market trends, job qualifications, and career pathways, into actionable insights for users.
The approach leverages principles of machine learning, hierarchical reinforcement learning, and retrieval-augmented generation to create an adaptive platform capable of personalizing recommendations and guiding users through informed decision-making towards career identification, readiness, and success.
Anticipated technical results include the development of scalable algorithms for hierarchical data modeling, a robust user-interface prototype, and pilot-tested outcomes demonstrating improved alignment between user preferences and career engagement.
The research will provide foundational advancements in integrating educational, psychological, and data science principles, with implications for creating more effective, scalable solutions in career discovery and workforce development.
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.
Subawards are not planned for this award.
The broader/commercial impact of this SBIR Phase I project is to ensure all Americans have access to 21st century careers while addressing a gap in workforce development through an adaptive, AI-driven career exploration platform which provides personalized career guidance for students in secondary and post-secondary education.
This unique approach to career readiness will reduce unemployment and skill mismatches across the nation by addressing critical stages in the career lifecycle of awareness, interest, and readiness.
By enhancing the technological infrastructure supporting career development and fostering a more adaptive, skilled, and competitive workforce, the project aligns with national interests towards accelerated access to meaningful career choices and lower rates of unemployment in critical areas.
The market opportunity for this project focuses on educational institutions and workforce development agencies, with the first market segment being high schools and post-secondary institutions.
By year three of deployment, this solution is projected to improve career readiness for thousands of individuals, improving labor market alignment, which will create significant economic value by creating a more engaged and aligned workforce.
This project will advance scientific understanding by developing and applying advanced learning theories and data-driven models to support open-field decision-making processes, fostering better career outcomes for all.
This Small Business Innovation Research (SBIR) Phase I project focuses on developing a data-driven platform to address the challenge of career discovery and decision-making in complex and evolving job markets.
The project integrates advanced learning theories to design state-of-the-art artificial intelligence and data visualization technologies to design and validate a novel system that contextualizes large datasets inclusive of such as labor market trends, job qualifications, and career pathways, into actionable insights for users.
The approach leverages principles of machine learning, hierarchical reinforcement learning, and retrieval-augmented generation to create an adaptive platform capable of personalizing recommendations and guiding users through informed decision-making towards career identification, readiness, and success.
Anticipated technical results include the development of scalable algorithms for hierarchical data modeling, a robust user-interface prototype, and pilot-tested outcomes demonstrating improved alignment between user preferences and career engagement.
The research will provide foundational advancements in integrating educational, psychological, and data science principles, with implications for creating more effective, scalable solutions in career discovery and workforce development.
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.
Subawards are not planned for this award.
Awardee
Funding Goals
THE GOAL OF THIS FUNDING OPPORTUNITY, "NSF SMALL BUSINESS INNOVATION RESEARCH / SMALL BUSINESS TECHNOLOGY TRANSFER PHASE I PROGRAMS", IS IDENTIFIED IN THE LINK: HTTPS://WWW.NSF.GOV/PUBLICATIONS/PUB_SUMM.JSP?ODS_KEY=NSF24579
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Ocala,
Florida
34471-8714
United States
Geographic Scope
Single Zip Code
Nextgenedu was awarded
Project Grant 2507751
worth $304,993
from National Science Foundation in April 2025 with work to be completed primarily in Ocala Florida United States.
The grant
has a duration of 1 year 3 months and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
The Project Grant was awarded through grant opportunity NSF Small Business Innovation Research / Small Business Technology Transfer Phase I Programs.
SBIR Details
Research Type
SBIR Phase I
Title
SBIR Phase I: An Adaptive AI-Driven Career Exploration Platform
Abstract
The broader/commercial impact of this SBIR Phase I project is to ensure all Americans have access to 21st century careers while addressing a gap in workforce development through an adaptive, AI-driven career exploration platform which provides personalized career guidance for students in secondary and post-secondary education. This unique approach to career readiness will reduce unemployment and skill mismatches across the nation by addressing critical stages in career lifecycle of awareness, interest, and readiness. By enhancing the technological infrastructure supporting career development and fostering a more adaptive, skilled, and competitive workforce, the project aligns with national interests towards accelerated access to meaningful career choices and lower rates of unemployment in critical . The market opportunity for this project focuses on educational institutions and workforce development agencies, with the first market segment being high schools and post-secondary institutions. By year three of deployment, this solution is projected to improve career readiness for thousands of individuals, improving labor market alignment, which will create significant economic value by creating a more engaged and aligned workforce. This project will advance scientific understanding by developing and applying advanced learning theories and data-driven models to support open-field decision-making processes, fostering better career outcomes for all.
This Small Business Innovation Research (SBIR) Phase I project focuses on developing a data-driven platform to address the challenge of career discovery and decision-making in complex and evolving job markets. The project integrates advanced learning theories to design state-of-the-art artificial intelligence and data visualization technologies to design and validate a novel system to contextualizes large datasets inclusive of such as labor market trends, job qualifications, and career pathways, into actionable insights for
Topic Code
LC
Solicitation Number
NSF 24-579
Status
(Ongoing)
Last Modified 4/4/25
Period of Performance
4/1/25
Start Date
7/31/26
End Date
Funding Split
$305.0K
Federal Obligation
$0.0
Non-Federal Obligation
$305.0K
Total Obligated
Activity Timeline
Additional Detail
Award ID FAIN
2507751
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
L7L9Q2PDHMV4
Awardee CAGE
8U7Q4
Performance District
FL-03
Senators
Marco Rubio
Rick Scott
Rick Scott
Modified: 4/4/25