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SBIR Phase II:A Digital Platform That Engages Elementary Aged Girls in STEM Through Personalized Informal Learning

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 2321914
Agency Tracking Number: 2321914
Amount: $999,800.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: LC
Solicitation Number: NSF 23-516
Solicitation Year: 2023
Award Year: 2023
Award Start Date (Proposal Award Date): 2023-10-01
Award End Date (Contract End Date): 2025-09-30
Small Business Information
Charlotte, NC 28269
United States
HUBZone Owned: Yes
Woman Owned: Yes
Socially and Economically Disadvantaged: Yes
Principal Investigator
 Abimbola Olukeye
 (704) 728-8439
Business Contact
 Abimbola Olukeye
Phone: (704) 728-8439
Research Institution

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase II project is an increase in the representation of women in Science, Technology, Engineering, and Math (STEM) employment areas, enabling the US to meet the increasing demand for STEM workers and maintain competitiveness in the global innovation community. The factors contributing to the underrepresentation of girls and women in STEM often take effect early in their education and extend beyond traditional classrooms settings. Very few solutions specifically address the support needed by parents to facilitate STEM informal learning in a way that is engaging to their young daughters. This project intends to deliver personalized learning pathways designed to catalyze positive STEM experiences for girls early in their learning journeys so that they are more likely to embrace STEM careers._x000D_
This project seeks to deliver a learning platform that operates using a novel recommender system, which applies algorithmic modeling of surprise and curiosity as well as best practices regarding the unique STEM learning needs of young girls. The main technical hurdles that will be addressed in this project are as follows: (1) refinement of algorithmic model, which will be applied to generate recommendation sequences that elicit curiosity in manner that both increases interest in STEM and prompts additional STEM learning and career awareness; (2) expansion of a dataset and data representation through the enhanced features and improvements to the data model; (3) visualization and gamification of learner interest inputs and (4) implementation of an engaging user interface and experience. The refinement of algorithmic models is expected to expand the research knowledge on recommendations for behavior change, recommender systems for a young target audience, and surprise and curiosity modeling in artificial intelligence systems.The solution will ultimately deliver a commercial application that personalizes STEM career exploration, particularly suited for young women._x000D_
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.

* Information listed above is at the time of submission. *

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