ASPIRE replaces traditional methods for annual training, pre-employment assessments, and role readiness assessments with an AI powered system. ASPIRE uses computer adaptive assessments and intensive automated analytics to narrow down on the very specific gaps a person might have relative to their specific role and their specific agency.  It then automatically generates a personalized learning pathway for that person, which is presented to them with multiple media through which to take each lesson needed to fill their specific gaps so as to best meet their learning needs. The system then validates the learning, and certifies the person’s readiness or rates their expertise, depending on the use case.

ASPIRE was designed and built by an interagency collaboration among VA, Navy, Air Force, and has been tested and championed by NASA, HHS, OPM, and Labor as well. Moreover, additional agencies are getting involved currently including Treasury and State. Its development was accelerated by a NAII Tech Sprint, which is itself an innovated and efficient acquisition and incubation methodology. At these and other points, its creation has been as innovative as the system itself.

This research is focused on a three-phased pilot structure consisting of: research (Phase I), research, pilot & MVP (Phase II), and research, agile development, and program of record (Phase III). 

Phase I focuses on developing empirically grounded models of the organizational requirements, knowledge and skills competency capture processes, technical system configurations, organizational contexts and their interrelationships that support filling VA ORD’s AI competency and workforce-draw needs.  Such a study must examine the work practices, processes, and sources necessary to capture critical talent management data, as well as the technical information infrastructure through which these artifacts are articulated and shared in parallel.  Studying any of these factors in isolation could lead to ineffective investments of time and resources and increase risk of project success as it discounts the significance of a socio-technical system.
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Research on the State of Play and Emergence of germane AI technologies, as well as assessment and education technologies and practices.

Leads: Tim Honaker (IAF) and Travis Linderman (IAF)

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Investigate the requirements for a common data mode, the key to any system of system integration in how the systems interact between components. The future integration of an Assessment tool into a Learning Management System, Competency Management System and the VA’s Personnel Management Component must be understood and pre-planned to assess the learning pathways desired.

Leads: Prof. Valdis Berzins (NPS)

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Investigate the VA requirements for an adaptive assessment tool to include usability, competency model, and integration requirements

Leads: Prof. Patrick McClure (NPS) and Tim Honaker (IAF)

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Investigate the requirements and current efforts supporting the President’s 2021 executive order communicating the need for increased security of the software supply chain.  Findings will identify the process and procedures necessary for the TEAMS assessment tool to meet the President’s requirements.  This is in response to the EO 14028)

Leads: Prof Britta Hale (NPS) and Prof. Chris Manuel (NPS)

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Investigate and define technical requirements for a personalized adaptive learning platform that can address the knowledge gaps identified by the assessment tool for the purpose of effective and timely professional development of VA personnel.  

Leads: Prof. Ralucca Gera (NPS) and Assistant Prof. Mark Reith (AFIT)

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  1. Observation: We All Need to Use AI
  2. Motivation: Knowledge Precedes Use
  3. Analyze: Know what we know
  4. Scope of Pilot Action: Know what you don't know: To discover and describe any agency’s AI attainment gap we must first develop a standard AI knowledge framework, then complete this knowledge assessment while also performing a survey of agency needs, and then analyze the data to identify, qualify, and quantify this gap: i.e. what we don’t know.

  

Research Plan


Phase II of the ASPIRE pilot will develop empirically grounded models of the organizational requirements, knowledge and skills competency capture processes, technical system configurations, organizational contexts and their interrelationships that support filling VA ORD’s AI competency and workforce-draw needs.  Such a study must examine the work practices, processes, and sources necessary to capture critical talent management data, as well as the technical information infrastructure through which these artifacts are articulated and shared in parallel.  Studying any of these factors in isolation could lead to ineffective investments of time and resources and increase risk of project success as it discounts the significance of a socio-technical system.
            
The tentative 15 research tasks for phase 2 are:
  1. Research on the current status of germane AI technologies, as well as assessment and education technologies and practices 
  2. Determination of Competency Framekwork for AI (CMS)
  3. Determination of VHA ORD AI Competency State 
  4. Learnind and Development System (LDS)
  5. Common Data Model Discovery & Testing (CDM)
  6. System Bill of Materials (SysBOM) and Automation of Software Bill of Materials (SBOM) Requirements and Audit Procedure 
  7. Development of Security Operations (DevSecOps)
  8. Technical Direction Agent (TDA)
  9. Diversity and Inclusion Talent Management Strategy 
  10. Augmented Reality (AR)/Virtual Reality Interface Requirements 
  11. Use-based Vulnerability Audit via Monterey Phoenix 
  12. Generative AI
  13. Acquisition Strategy Development
  14. AI-Enabled Prediction of Competency Alignment in Workforce Training and Education
  15. Learning effectivess of the ASPIRE system
The current research is sponsored by the National Artificial Intelligence Institute at the U.S. Department of  Veterans Affairs. 
modal disclaimer

  For information about the website, please email Prof. Ralucca Gera (rgera@nps.edu).