Teaching With AI, Not for AI

Preserving Human Judgment and Surfacing Human Biases in Public Administration Education

Bibliographic Data

ID22425952
AuthorsLaura Y Liu (0000-0002-3831-318X, University of Dayton, corresponding author), Grant W Neeley (0000-0003-2323-0848, University of Dayton)
Year2026
Publication date2026-06-18
Open AccessYes
TypeARTICLE
VenueJournal of Public Affairs Education (JOURNAL)
Journal identifiersISSN: 1523-6803 • E-ISSN: 2328-9643
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1177/15236803261460776
OpenAlexW7165152331
LanguageEN
References cited26

Artificial intelligence is reshaping public administration, yet public affairs curricula often stress tool proficiency and “human-in-the-loop” cautions without helping students examine how their own assumptions shape AI-assisted analysis. This paper uses learner-centered pedagogy that embeds AI as a “mirror” to surface human biases across problem framing, evidence use, alternative generation, and evaluation in a policy analysis assignment. Drawing on two cohorts in an MPA policy seminar, we compare no-AI-assisted and AI-collaborated cohorts and qualitatively analyze students’ policy analyses and reflections. The AI-collaborated cohort more consistently documented evidence verification, incorporated multiple perspectives, and produced more coherent justifications and comparative alternatives. A policy alternative assessment stage revealed students’ evaluative biases by contrasting their preferred options with AI-generated recommendations based on selected criteria and subsequent feasibility checks. Findings suggest that preserving human judgment does not require rejecting AI; reflective assignment design can build AI literacy and professional agency.

Curriculum · Education policy · Higher education · Literacy · Policy analysis · Public policy · Artificial Intelligence in Healthcare and Education · Computational and Text Analysis Methods · Ethics and Social Impacts of AI

Tools

Open DOI