Investigating the Impacts of Generative AI on Information Seeking

Alexi Orchard; Shannon Lodoen

cs.HCSep 13, 2026

Investigating the Impacts of Generative AI on Information Seeking

Authors: Alexi Orchard, Shannon Lodoen

Organizations: Technology and Digital Studies University of Notre Dame Notre Dame, IN, USA · Humanities and Communication Embry Riddle Aeronautical University Prescott, AZ, USA

Abstract

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.

Human-AI Interaction

Human-AI interaction studies how people and AI systems communicate, collaborate, make decisions, and shape one another over time.

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Delegating Before Learning: Where Generative AI Sits in Students' Professional Communication

Jared Ren, Soobin Cho

Human Centered Design & Engineering, University of Washington, Seattle, USA

Generative AI in Education Human-AI Collaboration Generative AI Adoption Text Generation

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We conducted an interview study with twelve students on their use of generative AI in academic communication. Students delegated professional messages to AI most where the pressure to sound professional is highest: email to instructors and administrators. AI involvement ranged from correcting the writer's own text to working out and writing the message outright, and students checked AI-written text against two criteria: whether it looks like AI and whether it sounds like them. Building on these findings, we model the AI-mediated process of writing a student--instructor email at the highest level of involvement we observed, and compare it with an unaided model of writing the same messages, built from participants' accounts and a classic model of the writing process. Three differences emerge: the learning loop that builds writing skill is removed, the message is no longer written for its specific recipient, and the confidence a successful exchange returns goes to using the system rather than to the writer's own ability. From these differences we derive two risks, that individual capacities never form and that authenticity and trust in communication become work. Design can respond to both but is unlikely to be enough, so the risks also need research and policy attention.

Abstract

Human-AI Interaction

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