Toward Integrating AI Chat and Search: A User-Centered Perspective across Age Groups
Year: 2025
Type: article
Abstract: People face challenges with complex search tasks daily; chat-based large language models have the potential to assist search by helping with query formulation and content navigation, for instance. However, there is a lack of user research on integrating AI chat and traditional search to enhance Information Retrieval (IR) for the general public. This study explores how younger (25 participants) and older (22 participants) adults utilize these two platforms to retrieve information. Through two IR tasks, quality- and quantity-oriented tasks, focused on gathering evidence related to various countries’ CO 2 emissions, younger users spent more time, issued more queries, and explored more external web pages than older adults in both tasks. In contrast, despite reporting less familiarity with AI chat, older adults relied more on chat to complete their tasks. They submitted longer queries for the quality task and reached the first query faster for the quantity task than their younger counterparts, reflecting a top-down IR strategy. In the end, task outcomes were similar for the two groups, indicating AI chat’s capability to support IR. Drawing from our study results and relevant literature, we propose five design requirements to harness the synergic value of search and AI chat in supporting IR.
DOI: 10.1145/3777486
Authors: Chen He, Michiel Spape, Khadijatul Kobra, Robin Welsch, Giulio Jacucci
Institutions: University of Helsinki, University of Macau, Aalto University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Strategic Research Council
OpenAlex ID: W4416415738