Information Foraging With Generative AI

Usage Patterns in Germany and Israel

Bibliographic Data

ID17823281
AuthorsInbal Klein-Avraham (0000-0002-2642-5686, Technion – Israel Institute of Technology, corresponding author), Evelyn Jonas (0009-0006-1942-4622, Technische Universität Braunschweig), Esther Greussing (0000-0001-8655-5119, Technische Universität Braunschweig), Monika Taddicken (0000-0001-6505-3005, Technische Universität Braunschweig), Ayelet Baram-Tsabari (0000-0002-8123-5519, Technion – Israel Institute of Technology)
Year2026
Volume14
Publication date2026-04-02
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueMedia and Communication (JOURNAL)
Journal identifiersISSN: 2183-2439 • E-ISSN: 2183-2439
PublisherCogitatio (PUBLISHER • PT)
DOI10.17645/mac.11487
OpenAlexW7146957778
LanguageEN
Citations received2
References cited48

Generative artificial intelligence (GenAI) alters how people seek information, regardless of its susceptibility to epistemic limitations such as producing inaccurate or biased information. Studies of individuals’ usage patterns of GenAI for accessing information remain scarce. Here, we examined how individuals perceive and use GenAI for various information purposes and at different complexity levels across cultures. Based on online surveys of representative samples from Germany ( N = 562) and Israel ( N = 500), the findings showed that Germans rated GenAI higher in providing comprehensive information, whereas Israelis perceived GenAI as more responsive to users’ information needs. Latent class analysis (LCA) of regular GenAI users (Germany: n = 159; Israel: n = 254) identified culturally distinct user profiles: three in Israel (e.g., Favoring Pragmatists, Reserved Experts, and Skeptical Minimalists) and four in Germany (e.g., Naïve Enthusiasts, GenAI-Savvy Abstainers, Cautious Skeptics, and Passive Optimists). Harnessing the information foraging theory, we focused on the diverging balance between the currencies (perceived benefits, i.e., responsiveness of GenAI and comprehensibility of its content), costs (epistemic AI knowledge, i.e., awareness of GenAI’s limitations), and “forager attributes” (previous experience with GenAI and knowledge of its workings). The information foraging theory prism highlighted two cross-cultural similarities: the avoidance pattern of users reporting low perceived benefits, and the inclination to utilize GenAI for more complex and risk-involving science-related information, characterizing users who demonstrated high perceived benefits and low epistemic knowledge

Cognition · Foraging · Generative grammar · Latent class model · Skepticism · AI in Service Interactions · Artificial Intelligence in Healthcare and Education · Ethics and Social Impacts of AI

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