Evaluating Adaptive AI for Contracting Officer Readiness: Design and Pedagogical Proposal for the Warrant Board RAG Chatbot
Name
Grosvenor_Evaluating Adaptive AI for Contracting Officer Readiness.pdf
Description
Main report
Size
355.64 KB
Format
Adobe PDF
Checksum (MD5)
b4763bb96a366a8561111aebc0e5a6bd
Author(s)
Mullen, Julia
Grosvenor, Sarah
Date Issued
March 20, 2026
Abstract
The United States Air Force (USAF) requires a
sustained and expanding pool of warranted Contracting Officers
(COs) to meet growing operational and fiscal demands across
its global enterprise. The authority to obligate funds and bind
the government contractually—granted through the issuance
of a warrant—requires successful completion of a multi-stage
evaluation process culminating in a scenario-based oral board.
This final interview assesses a candidate’s ability to interpret
and apply acquisition policy under complex and ambiguous
conditions.
This paper proposes the design of an adaptive artificial
intelligence (AI) training system—the Warrant Board Retrieval-
Augmented Generation (RAG) Chatbot—to serve as a simulated
board-preparation environment. This chatbot inverts the common
’user question, AI answer’ model, and instead has the
chatbot ask the learner a series of critical thinking scenariobased
questions. The prototype design adopts a model-agnostic
LLM gateway capable of operation through either commercial
APIs (e.g., OpenAI) or secure, government-hosted environments
such as GenAI.mil, ensuring accessibility within unclassified Air
Force networks. This research contributes to the emerging field of
AI-assisted professional education by developing a transparent,
auditable, and pedagogically grounded framework for formative
learning in acquisition training.
Subjects
Adaptive AI training system
AI assisted professional education
LLM assisted education
MIT Department
Lincoln Laboratory
Persistent DSpace Link
