DOMO ARIGATO MR. ROBOTO: LEVERAGING ARTIFICIAL INTELLIGENCE TO IMPROVE DEVELOPMENT OF NON-EMERGENCY LEADERSHIP SKILLS IN THE FIRE SERVICE
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Authors
Freeman, Caleb D.
Subjects
leadership
leadership development
framework
artificial intelligence
simulation
virtual agent
problem-based learning
PBL
action learning
leadership development
framework
artificial intelligence
simulation
virtual agent
problem-based learning
PBL
action learning
Advisors
Matei, Cristiana
Morag, Nadav
Date of Issue
2024-03
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
The U.S. fire service faces the formidable challenge of overcoming deficiencies in leadership skills in non-emergency situations. This thesis investigates the potential of artificial intelligence (AI) to revolutionize simulation training that improves leadership development. It confronts the pressing need for innovative training methodologies that go beyond traditional approaches, proposing a novel integration of evidence-based leadership frameworks with cutting-edge AI technologies. The findings recommend Scott’s integrative problem-based learning (PBL)/action learning (AL) framework alongside AI to create a promising pathway to more dynamic, interactive, and realistic training experiences. The research highlights a transformative approach that could redefine leadership training, recommending the adoption of AI-enhanced simulations to achieve improved leadership outcomes. These simulations aim to develop crucial leadership competencies, offering a forward-looking solution to address current inadequacies. The recommendations shed light on the untapped potential of AI in leadership development and set the stage for a new era in fire service training. The future of U.S. fire service leadership development lies in collaborative cooperation by industry leaders to exploit the intersection of technology and training delivery.
Type
Thesis
Description
Series/Report No
Department
National Security Affairs (CHDS)
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NPS Report Number
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Citation
Distribution Statement
Distribution Statement A. Approved for public release: Distribution is unlimited.
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