‘AI 행정공학’의 개념화와 공공부문 적용 가능성에 관한 연구
A Study on the Conceptualization of AI-based Administrative Engineering and Its Applicability to the Public Sector
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(Purpose) This study aims to empirically examine the research landscape of Korean public administration from 2015 to 2025 and, based on this diagnosis, to conceptualize AI Administrative Engineering as a new methodological paradigm for addressing complex public problems. (Design/methodology/approach) The study applies text mining and topic modeling to more than 2,800 abstracts from five major Korean public administration journals. To move beyond descriptive analysis, expert focus group discussions and prior research are used to identify 25 core variables across five domains: internal organizational operations, citizen service experience, education and capability, data and AI infrastructure, and institutions and governance. These variables are integrated into a system dynamics causal loop diagram and simulated using a Powersim-based model to compare alternative reform scenarios. (Findings) The results show that existing research remains heavily focused on traditional themes such as policy, institutions, organizations, local government, and welfare, while computational, design-oriented approaches are largely absent. Simulation results indicate that under a status quo scenario, improvements in administrative performance and citizen satisfaction are marginal, whereas an integrated reform package yields substantial nonlinear improvements. (Research implications or Originality) The study identifies a structural gap between explanatory research and practical problem-solving needs and proposes AI Administrative Engineering as an original framework to transform public administration into a more design- and execution-oriented discipline.
Abstract
(Purpose) This study aims to empirically examine the research landscape of Korean public administration from 2015 to 2025 and, based on this diagnosis, to conceptualize AI Administrative Engineering as a new methodological paradigm for addressing complex public problems. (Design/methodology/approach) The study applies text mining and topic modeling to more than 2,800 abstracts from five major Korean public administration journals. To move beyond descriptive analysis, expert focus group discussions and prior research are used to identify 25 core variables across five domains: internal organizational operations, citizen service experience, education and capability, data and AI infrastructure, and institutions and governance. These variables are integrated into a system dynamics causal loop diagram and simulated using a Powersim-based model to compare alternative reform scenarios. (Findings) The results show that existing research remains heavily focused on traditional themes such as policy, institutions, organizations, local government, and welfare, while computational, design-oriented approaches are largely absent. Simulation results indicate that under a status quo scenario, improvements in administrative performance and citizen satisfaction are marginal, whereas an integrated reform package yields substantial nonlinear improvements. (Research implications or Originality) The study identifies a structural gap between explanatory research and practical problem-solving needs and proposes AI Administrative Engineering as an original framework to transform public administration into a more design- and execution-oriented discipline.