Source-linked AI summary
Investigating the Impacts of Generative AI on Information Seeking
Alexi Orchard, Shannon Lodoen
TL;DR
The paper asks how generative AI’s growing role in information seeking affects expertise, authority, and trust. It applies procedural rhetoric to examine how chatbot interaction structures users’ engagement with information. It describes risks from streamlined, narrowing information access alongside a case where simplified judicial summaries helped non-experts understand a ruling.
Problem
Generative AI chatbots are reconfiguring information seeking, raising questions about expertise, authority, and how users assess chatbot-provided information.
Source: §1 · p. 1Method
The paper applies procedural rhetoric to examine chatbot information-seeking interactions and how procedures shape users’ engagement with expert information.
Source: §1 · p. 1, §2.1 · p. 2Results
Chatbots can reduce or eliminate source-gathering and sifting, potentially narrowing users’ understanding, while an AI assistant simplifying judicial opinions helped non-experts understand key features of a ruling.
Source: §3 · p. 3Takeaways & Limitations
Understanding information-seeking processes is crucial to assessing how generative AI influences users’ perceptions, perspectives, and practices.
Source: §3 · p. 3
Abstract
from arXiv · showhide
This paper is an encore submission of our 2026 journal article "Expertise and Information Seeking in the Age of Generative AI: New Procedures, New Problematics" with an extended discussion for the CSCW 2026 "Broader Impacts of GenAI in Communication" Workshop on October 10, 2026. In the original article, we employ procedural rhetoric to analyze how generative AI chatbots leverage natural language signifiers of expertise and intelligence to influence users' perception of their trustworthiness. In this submission, we extend our conversation in the CSCW community with the goal of cultivating a cross-disciplinary vocabulary for describing, analyzing, and mitigating the risks posed by the integration of generative AI into human communication practices. It is important to develop an understanding of how the procedures surrounding information-seeking practices are informed by users' values, experiences, and expectations - and how these procedures might in future be altered by the emerging turn toward AI "experts" and authority.
1 Introduction
Generative AI chatbots are reconfiguring information seeking and raising questions about expertise, authority, and trust. The paper examines how interaction procedures—not just generated content—shape users’ engagement with expert information, using a cross-disciplinary rhetorical lens.
1 Introduction
- The authors describe information seeking as a long-standing human activity whose methods have evolved alongside changing ways of codifying and collecting information.Traditional practices include consulting reputable sources such as newspapers, doctors, and trusted neighbors; generative AI introduces new complexities.Source: §1 · p. 1
- Generative AI chatbots have begun fulfilling varied roles for human users, from virtual assistants to tutors, raising questions about expertise, authority, and information seeking.These questions are being taken up across disciplines beyond AI and computing, including law, politics, medicine, and the humanities,, [6],,.Source: §1 · p. 1
- Research at the intersection of rhetoric and generative AI has largely focused on prompts and generated outputs, leaving chatbot information-seeking procedures less examined.Prior work described procedural persuasion across information seeking, language construction, and social relationship building; this paper focuses on seeking, accessing, and using “expert” information,.Source: §1 · p. 1
- The authors combine scholarship across computer science, science communication, and rhetorical studies to show how humanities scholarship can inform ethical analyses of emerging technologies.They address researchers, educators, and everyday chatbot users affected by changes in information seeking.Source: §1 · p. 2
2 Key Concepts
The paper uses procedural rhetoric to examine how interaction rules structure information seeking and how users may grant AI systems authority. It places these questions within changing information-seeking practices and contested ideas of expertise.
2.1 Procedural Rhetoric
- Procedural rhetoric examines how rule-based interactions persuade through available choices, required steps, and levels of ease, including the procedures encoded in chatbot responses.The authors use this lens to study how procedures shape information seeking and how users construct meaning through interaction.Source: §2.1 · p. 2
2.2 Information Seeking
- Information-seeking methods evolve with technology, while chatbots turn a query into a human-like exchange that can be refined through follow-up prompts.Unlike a search that yields pages of sources, the chatbot can provide a response and increasingly specific answers as the user clarifies the query.Source: §2.2 · pp. 2–3
2.3 Expertise
- Although AI systems can engage in problem-solving resembling human information seeking, this does not establish that they are experts or that their outputs should be accepted unquestioningly.The section frames expert status as involving audience recognition, cultivated skills, deliberation, and moral judgment [22, p. 3].Source: §2.3 · p. 3
3 Relevance to the CSCW Community
The authors argue that chatbot-mediated information seeking changes how users encounter, evaluate, and understand information. They point to both risks in streamlined access and a use case in which simplified AI summaries help non-experts understand judicial opinions.
3 Relevance to the CSCW Community
- The authors raise questions about trust, delegated information filtering, and reliance on systems without true experience or direct knowledge of the world they describe.They connect these concerns to the future of information seeking and communication practices.Source: §3 · p. 3
- Chatbots can locate, assess relevance, summarize, and present information, reducing or eliminating users’ source-gathering and sifting.The authors warn that this can narrow understanding toward popular or generic information, while diverse perspectives may go unnoticed unless users know how to prompt for them; prior work links this shift to concerns about critical thinking, misinformation, bias, and high-stakes veracity [35],,,.Source: §3 · p. 3
- An AI assistant that simplified judicial opinions was shown to help non-experts understand key features of a ruling.In specialized fields, users may value an “almost human explanation” that avoids discomfort associated with jargon or unclear resolutions, potentially reshaping expectations of technical knowledge.Source: §3 · p. 3
- The authors propose rhetoric, expertise, and information seeking as a vocabulary for examining emerging risks in AI-mediated information seeking.They argue that understanding users’ processes is crucial to assessing how generative AI influences perceptions, perspectives, and practices [24, p. 340].Source: §3 · p. 3