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AI-Based Learning Environment (ABLE) for Undersea Warfare (USW) Training

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-23-C-0652
Agency Tracking Number: N23A-T014-0227
Amount: $137,834.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N23A-T014
Solicitation Number: 23.A
Timeline
Solicitation Year: 2023
Award Year: 2023
Award Start Date (Proposal Award Date): 2023-08-09
Award End Date (Contract End Date): 2024-02-06
Small Business Information
9180 Brown Deer Road
San Diego, CA 92121-2238
United States
DUNS: 131182388
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Bradley Monk
 (858) 535-1661
 bradley.monk@pacific-science.us
Business Contact
 Ronald Moore
Phone: (858) 535-1661
Email: ron.moore@pacific-science.us
Research Institution
 SRI International Computer Science Laboratory
 Thomas Neimeyer
 
201 Washington Rd
Princeton, NJ 08540-6449
United States

 (609) 734-2654
 Domestic Nonprofit Research Organization
Abstract

To compete on the world stage of undersea warfare (USW), the US Navy’s USW systems are frequently updated with advanced capabilities. As a result, modernization trainers need to perform the challenging tasks of updating training material to reflect the new (and obsolete) capabilities. This process requires comparing legacy to updated documentation, identifying changes to system capabilities, and updating curricula and exams to reflect those changes. These tasks are currently all performed manually, consuming significant time and effort. Pacific Science and Engineering (PSE) and SRI International (SRI) International propose to develop an artificial intelligence (AI)-based learning environment (ABLE) to support Navy instructors responsible for modernization training. ABLE will support the ingestion of relevant digital documentation and use natural language processing (NLP) to analyze and extract content relevant to USW mission support. ABLE will also synthesize test questions and answers based on imported content, deliver, and grade exams, and summarize student performance. Importantly, ABLE will feature an intuitive user interface (UI) that will make it easy for trainers to validate and maintain updated curricula for USW operational training. ABLE’s NLP engine will be developed based on extensive testing and validation using state-of-the-art methods and deep understanding of the USW training domain, while its UI will be grounded in an evidence-based, user-centered design process and scientific application of human factors and cognitive science principles.

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

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