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

2026-04-11

AI Summary

This study investigates the impact of generative AI on knowledge diversity in online information seeking. Leveraging over 200,000 real human–AI dialogues, it introduces “searchability” as a novel dimension to analyze diversity in user queries and system responses, integrating measures of information diversity, a searchability taxonomy, and comparative experiments between ChatGPT and Google. The findings reveal that nearly 80% of ChatGPT queries are non-searchable and cover broader topics; however, for comparable searchable queries, ChatGPT’s responses exhibit significantly lower diversity than those from traditional search engines. Moreover, the AI’s outputs strongly shape the diversity of users’ subsequent explorations, uncovering a feedback mechanism inherent in human–AI interaction.

Technology Category

• Humans and AI: AI for Accessibility
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• generative AI
• information diversity
• online information seeking
• searchability
• knowledge exploration

Innovation

• generative AI
• information diversity
• searchability
• conversational inquiry
• human-AI interaction