The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
ChatGPT on ChatGPT: An Exploratory Analysis of its Performance in the Public Sector Workplace
Abstract
This study explores the impact of Generative Artificial Intelligence (GenAI), in particular, ChatGPT, on the public sector workforce in the United States, focusing on task replacement, assistance potential, and the evolving landscape of skills. Utilizing GPT-4 to evaluate 1,022 core tasks across 51 public sector occupations, we provide an exploratory analysis of the roles susceptible to ChatGPT automation and those in which ChatGPT can augment human efforts. Our findings reveal that while 63% of tasks are resistant to ChatGPT replacement, primarily due to their requirement for physical presence, emotional intelligence, and complex decision-making, tasks that are routine, rule-based, and involving basic content generation show a high potential for automation. The study also identifies key skills that will remain vital, those likely to become obsolete, and new skills that will emerge as essential, highlighting the need for a strategic approach to workforce development in the face of AI advancements. In particular, our findings underscore the growing importance of skills in applying AI technologies and the ability to validate and interpret AI-generated content for humans to remain competitive. We offer insights into public-sector-specific impacts and propose a methodological framework for future research, emphasizing the importance of adapting educational curricula and policies to prepare for an AI-integrated future.
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“This might be due to the fact that public sector entities prioritize values such as transparency, and equity, embedding these principles into the design and implementation of AI systems (Wang et al., 2024).”
From Exploring the Challenges of Generative AI on Public Sector Communication in Europe (2025) · cites this paper as Wang et al., 2024
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“Automation, encompassing AI and robotics, is altering job roles, skill requirements, and overall work dynamics in industries such as manufacturing, transportation, education, and even scientific research [1, 13, 17, 31, 45].”
GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models
Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2023) · arXiv · doi:10.48550/arxiv.2303.10130 · Cited by 2,330
Conceptualizing AI literacy: An exploratory review
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100041 · Cited by 2,205 · cited 1×
“This proficiency is encapsulated by the concept of “AI literacy,” which encompasses understanding, applying, evaluating AI tools, and ethical considerations [35].”
Artificial hallucinations in ChatGPT: implications in scientific writing
Alkaissi, H., & McFarlane, S. I. (2023) · Cureus · doi:10.7759/cureus.35179 · Cited by 1,540
Artificial Intelligence and the Public Sector—Applications and Challenges
Wirtz, B. W., Weyerer, J. C., & Geyer, C. (2019) · International Journal of Public Administration · doi:10.1080/01900692.2018.1498103 · Cited by 1,341 · cited 1×
“The U.S. federal government, for instance, has increasingly integrated AI-powered technologies into a wide array of public services, including healthcare, education, housing, and social benefits [2, 15, 34, 38, 43, 47].”
What can machine learning do? Workforce implications
Brynjolfsson, E., & Mitchell, T. (2017) · Science · doi:10.1126/science.aap8062 · Cited by 1,131 · cited 1×
“Contrary to earlier beliefs that AI would mainly displace low-skilled workers, recent studies suggest that GenAIs might offer significant benefits to mid-or low-skilled workers, enabling them to produce content that meets average standards [8, 10, 22, 42].”
Zuiderwijk, A., Chen, Y.-C., & Salem, F. (2021) · Government Information Quarterly · doi:10.1016/j.giq.2021.101577 · Cited by 795
Government by Algorithm: Artificial Intelligence in Federal Administrative Agencies
Engstrom, D. F., Ho, D. E., Sharkey, C. M., & Cuéllar, M.-F. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3551505 · Cited by 262
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Lee, U., Jung, H., Jeon, Y., Sohn, Y., Hwang, W., Moon, J., & Kim, H. (2024) · Education and Information Technologies · doi:10.1007/s10639-023-12249-8 · Cited by 217
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Cited by
Exploring the Challenges of Generative AI on Public Sector Communication in Europe
Lovari, A., & De Rosa, F. (2025) · Media and Communication · doi:10.17645/mac.9644 · Cited by 22
Metadata
- Authors
- Wang, J., Kiran, E., Aurora, S. R., Simeone, M., & Lobo, J.
- Affiliations
- Decision Theater, Arizona State University
- School of Applied Professional Studies, Arizona State University
- Robert C. Vackar College of Business and Entrepreneurship, The University of Texas Rio Grande Valley
- School of Complex Adaptive Systems, Arizona State University
- School of Sustainability, Arizona State University
- Venue
- Digital Government: Research and Practice
- Published
- 2025-06-16
- Volume
- 6
- Issue
- 2
- Pages
- 1-28
- ISSN
- 2691-199X
- 2639-0175
- Cited by
- 6
- References
- 13
- Language
- en
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