From Searchable to Non-Searchable: Generative AI and Information Diversity in Online Information Seeking

Abstract

Conversational generative AI systems such as ChatGPT are transforming how people seek and engage with information online. Unlike traditional search engines, these systems support open-ended, conversational inquiry, yet it remains unclear whether they ultimately expand or constrain the diversity of knowledge that users encounter in online search spaces, a primary foundation for knowledge work, learning, and innovation. Using over 200,000 real-world human-ChatGPT interactions, we examine how generative-AI-mediated inquiry reshapes diversity in both user inputs and system outputs through the lens of searchability - whether queries could plausibly be answered by traditional search engines. We find that almost 80% of ChatGPT user queries are non-searchable and span a broader knowledge space and topics than searchable queries, indicating expanded modes of inquiry. However, for comparable searchable queries, AI responses are less diverse than Google search results in the majority of topics. Moreover, the diversity of AI responses predicts subsequent changes in users' inquiry diversity, revealing a feedback loop between AI outputs and human exploration. These findings highlight a tension between expanded inquiry and constrained information exposure, with implications for designing hybrid search and generative-AI systems that better support exploratory knowledge seeking.