AMTP Proceedings 2026
Document Type
Conference Proceeding
Publication Date
Spring 2026
Abstract
Professional public servants (PSs) including police officers, postal workers, and government tax office and DMV employees interact face-to-face with citizens/persons of interest (POIs), in encounters that are frequently recorded using mobile phones and publicized on platforms such as YouTube.com. Building on our observations about recurrent PS–POI scripts and LLM-supported analysis, we develop five grounded propositions linking enacted scripts to existing theory. The propositions focus on describing early turn-taking asymmetry, the importance of sequential detail beyond-shallow analysis, the predictive role of script recurrence, the value of rich, script-based role-play, and the potential of narrative-intelligence databases for institutional learning. Instead of treating viral PS–POI video clips as episodic scandals, we argue they serve as data-rich laboratories for training, policy design, and research on human–AI conversation analysis in public service contexts.
Recommended Citation
Woodside, Arch G. and Sood, Suresh, "Conversation Analysis of Public-Servant and Persons-of-Interest Face-to-Face Talk via LLM‑Supported AI Script Mapping Tools" (2026). AMTP Proceedings 2026. 78.
https://digitalcommons.georgiasouthern.edu/amtp-proceedings_2026/78
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