Work

Users’ prompting strategies and ChatGPT’s contextual adaptation shape conversational information-seeking experiences

Year: 2026

Type: article

Abstract: Conversational AI, such as ChatGPT, is increasingly used for information seeking. However, little is known about how ordinary users actually prompt and how ChatGPT adapts its responses in real-world conversational information seeking (CIS). In this study, a nationally representative sample of 937 U.S. adults engaged in multi-turn CIS with ChatGPT on both controversial and non-controversial topics across science, health, and policy contexts. We analyzed both users' prompting strategies and the communication styles of ChatGPT's responses. The findings revealed behavioral signals of digital divide: only 19.1% of users employed prompting strategies, and these users were disproportionately more educated and Democrat-leaning. Further, ChatGPT demonstrated contextual adaptation: responses to controversial topics contain more cognitive complexity and more external references than to non-controversial topics. Notably, cognitively complex responses were perceived as less favorable but produced more positive issue-relevant attitudes. This study highlights disparities in user prompting behaviors and shows how user prompts and AI responses together shape information-seeking with conversational AI.

Source: Scientific Reports

DOI: 10.1038/s41598-026-42465-4

Authors: Haoning Xue, Yoo Jung Oh, Xinyi Zhou, Xinyu Zhang, Berit Oxley

Institutions: University of Utah, Michigan State University, Boise State University, Emory University

Language: English

Topic: Information Retrieval and Search Behavior

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,690

Funder: University of Utah