Whether It’s Searchable or Not: How Generative AI Affects Finding Diverse Information Online

Why Read This from an HCI Perspective

This article shows generative AI not as a ‘smarter search engine,’ but as an interaction tool that changes the information-seeking experience. It’s important that it examines how users ask questions, what answers they receive, and how their subsequent exploration changes. In particular, the approach of separating searchable questions from non-searchable ones offers strong implications for both UX design and HCI research, because it empirically demonstrates that while AI can broaden the scope of exploration, it may also reduce information diversity.

CIT's Commentary

The core of this study is not the model’s raw performance, but how the ‘back-and-forth between questions and answers’ changes users’ exploration. The finding that 79% of questions are difficult to handle well with traditional search suggests that people are using AI as a wider space for imagination than a search box. Conversely, the fact that AI answers to the same questions are less diverse than search results shows that a comfortable conversation can actually narrow one’s view. Therefore, an important design challenge is not to stop at providing good answers, but to reveal system state and create intervention paths that let users re-enter exploration from different directions. If this balance breaks down in hybrid search or AI agents, convenience may increase while the breadth of exploration could shrink.

Questions to Consider While Reading

• Q.How can we detect—at the interface level—the moment when AI reduces answer diversity, and make that visible to users?
• Q.How should the criteria for distinguishing searchable from non-searchable questions be designed differently in product contexts?
• Q.If answer diversity affects the diversity of the next questions, what UX metrics should be designed to measure it?