[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82799-en":3,"doc-seo-82799-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82799,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","The New Shape of Search: How Conversational AI Recomposes Information Seeking","Classic information seeking treats search as an iterative foraging loop: formulate a keyword query, scan results, reformulate, gather across sources, and synthesize. This study examines how inserting a conversational assistant reshapes that episode’s structure. Using an opt-in cross-surface panel that links real chat with the same users’ searches and browsing, the work finds bifurcation rather than uniform collapse: about 59% terminate in place while ~32% scaffold into longer multi-step journeys.","The New Shape of Search: How Conversational AI Recomposes  \nInformation Seeking  \nMichael Iannelli  \nScrunch AI New York, USA [michael@scrunchai.com](michael@scrunchai.com)  \nAlan Ai  \nScrunch AI New York, USA [alan.ai@scrunchai.com](alan.ai@scrunchai.com)  \narXiv :2607 .04282v 1 [ cs .HC] 5 Jul 2026  \nAbstract  \nClassic models cast information seeking as iterative foraging: formulate a keyword query, scan results, reformulate, gather across sources, synthesize. We ask what happens to that episode when a conversational assistant is inserted into it, and find it changes the shape of information seeking, not merely its volume. Linking real conversations with the major assistants to the same users’ searches and browsing (an opt-in cross-surface panel), and reconstructing the full cross-surface episode rather than a single query, we find conversational AI does not uniformly collapse the episode so much as bifurcate it. Most AI episodes (≈59%) terminate in place, no onward search or content step follows in the observed trace, while roughly a third (≈32%) scaffold into a longer multi-step journey. Which shape occurs is governed less by what the ask is about than by how much is asked: under task labels benchmarked against a three-model annotation panel, collapse is statistically indistinguishable across lookup, learning, and comparison episodes, yet falls monotonically with the length of the opening ask, from 72% atone-to-three words to 48% beyond twenty, a ∼12-word naturallanguage ask replacing iterated ∼3-word keyword queries. Roughly two-fifths of assistant episodes are workbench use, drafting, coding, editing, not information seeking at all, and these collapse most. What conversational AI does not do is displace search: search remains woven through roughly three-quarters of within-episode transitions, after reading a page the user returns to the search box over the assistant 70/30, and within-user search share does not fall. Verification, however, is rare, searches bearing explicit verification language follow ∼1% of episodes, and citation-forward interfaces do not measurably increase checking. All of this is episode structure, a compositional object we can identify without a demand counterfactual. Conversational AI recomposes the seeking episode: it answers brief asks in place and anchors invested asks in longer journeys, adding a layer rather than replacing search.  \nCCS Concepts  \n• Information systems → Users and interactive retrieval; Search interfaces; • Human-centered computing → Empirical studies in HCI.  \nKeywords  \nconversational AI; answer engines; generative AI; information seeking behavior; search episodes; exploratory search; information foraging; log analysis; sessionization; session analysis; cross-surface behavior; clickstream; burst-selection  \n1 Introduction  \nA person wants to understand a medical result, choose a car seat, or follow a breaking story. In the model that has organized information-retrieval research for decades, they begin in uncertainty, issue a keyword query, scan a ranked list, reformulate, gather across several sources, and synthesize, an iterative, multi-step episode rather than a single lookup [6, 23, 26] . Conversational AI is now inserted into that episode. The dominant framing of what it does is the answer engine: a prompt goes in, a synthesized answer comes out, the episode ends. That framing predicts collapse, the assistant absorbs the episode and the downstream search, comparison, and reading do not happen; nobody clicks through.  \nWe ask a more structural question than “how much.” Using an opt-in panel that links real conversation content to the same users’real search and browsing, we reconstruct the whole cross-surface episode, the chat, the searches, and the site visits that belong to one bout of seeking, and ask how its shape changes. The question is deliberately compositional. Whether AI creates net-new seeking is a demand counterfactual we cannot identify on observational data: ","cbCaiu3eVTNlNUjn","https://ap.wps.com/l/cbCaiu3eVTNlNUjn","pdf",556333,1,7,"English","en",105,"# Abstract\n# Introduction\n## Problem framing: answer-engine vs episode structure\n## Method: opt-in cross-surface episode reconstruction\n## Findings: bifurcation, task-label effects, and depth of the ask\n## Implications: search is not displaced","[{\"question\":\"How does conversational AI change the structure of information seeking episodes?\",\"answer\":\"It bifurcates them rather than uniformly collapsing them: many episodes end with an in-place assistant answer, while a substantial portion leads into longer multi-step journeys that continue beyond the chat.\"},{\"question\":\"What method is used to connect conversations with search activity?\",\"answer\":\"The study uses an opt-in cross-surface panel that links real conversation content to the same users’ search queries and site visits, enabling reconstruction of the full cross-surface episode.\"},{\"question\":\"Does conversational AI replace search or keep it in the process?\",\"answer\":\"Search remains woven through within-episode transitions (about three-quarters), and within-user search share does not decrease, though explicit verification with citation-like checking is 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does conversational AI change the structure of information seeking episodes?","Question",{"text":75,"@type":76},"It bifurcates them rather than uniformly collapsing them: many episodes end with an in-place assistant answer, while a substantial portion leads into longer multi-step journeys that continue beyond the chat.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What method is used to connect conversations with search activity?",{"text":80,"@type":76},"The study uses an opt-in cross-surface panel that links real conversation content to the same users’ search queries and site visits, enabling reconstruction of the full cross-surface episode.",{"name":82,"@type":73,"acceptedAnswer":83},"Does conversational AI replace search or keep it in the process?",{"text":84,"@type":76},"Search remains woven through within-episode transitions (about three-quarters), and within-user search share does not decrease, though explicit verification with citation-like checking is 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