Investigating the Impacts of Generative AI on Information Seeking
cs.HC · cs.AI · cs.CY
Quick verdict
Encore workshop essay arguing chatbot procedures rhetorically manufacture perceived expertise; coherent conceptual frame, but presents no empirical evidence.
Summary
This paper is an encore submission to the CSCW 2026 workshop on the Broader Impacts of GenAI in Communication, extending the authors' 2026 IEEE Transactions on Technology and Society article. It applies Ian Bogost's concept of procedural rhetoric — persuasion through rule-based representations and interactions — to generative AI chatbots, arguing that the interactional procedures surrounding chatbot use (rather than response content) function as rhetorical arguments that shape users' perceptions of the systems' expertise and trustworthiness. Section 2 builds three conceptual pillars: procedural rhetoric (2.1), information seeking as defined by Kuhlthau and situated in library and information science (2.2), and expertise as negotiated social recognition per Collins and Evans (2.3). Section 3 argues that chatbots replace search-and-recall with pre-located, pre-summarized delivery, potentially eliminating the source-evaluation practices that build critical discernment and narrowing users' exposure to diverse perspectives; a running Google-versus-chatbot headache-query example illustrates the shift. The method is purely conceptual literature synthesis: no empirical data, no analysis of specific systems, no evaluation. The paper's value is as a position piece offering shared vocabulary for cross-disciplinary risk analysis; its principal limitations are the absence of evidence for its behavioral claims and substantial overlap with the parent journal article.
Review complete
Assessment at a glance
3
risk flags identified
● high 0
● medium 2
● low 1
4 strengths · 5 weaknesses · 5 author questions
AI text signal = Binoculars zero-shot detector score over 20 sampled passages (Fast-DetectGPT co-reported) — absence does not prove human authorship, and a flag does not imply misconduct.
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Key claims under review
- The interactional procedures of AI chatbots — not the content of their responses — constitute rhetorical arguments that persuade users to reconfigure their information-seeking practices.
- Chatbots display natural-language signifiers of expertise and intelligence that inflate users' perception of the systems' trustworthiness, despite the systems lacking the experience-and-recognition pathway that defines expertise ([22], [31], [32]).
- The shift from search-and-recall to chatbot-delivered, pre-summarized information reduces or eliminates the source-evaluation practices that build users' capacity to discern relevance and credibility (Section 3).
- Procedural rhetoric offers CSCW a cross-disciplinary vocabulary for describing, analyzing, and mitigating the risks of generative AI in communication practices.
Strengths
Rigorous and accurate theoretical grounding
Section 2.1 faithfully reconstructs Bogost's procedural rhetoric with page-cited quotations ([24] pp. 5, 7, 8, 340) and makes a non-obvious transfer of the framework from videogames to chatbot interaction, where processes 'found the logics that structure behavior.'
Genuinely interdisciplinary synthesis
The paper bridges the LIS canon (Kuhlthau [29], Savolainen [28], Taylor [26]), expertise studies (Ericsson [31], Collins & Evans [32], Mehlenbacher [22]), and current empirical HCI work on LLM persuasion and misinformation ([4], [12], [19], [38]) — a combination rarely assembled in rhetoric-oriented writing.
Concrete comparative illustration
Clear stake articulation with testable hypotheses embedded
Weaknesses
No empirical basis for behavioral claims
Substantial unquantified overlap with parent article
No object-level analysis of actual systems
Unreconciled counter-evidence
No stated selection method for the literature
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Preliminary Review: Investigating the Impacts of Generative AI on Information Seeking
The remaining sections cover Methodology Assessment, Statistical Evidence, Results & Reproducibility, Limitations & Risks, and Verdict & Recommendations.
- All 4 strengths & 5 weaknesses, analyzed in detail
- 3 risk flags with severity, section & reviewer questions
- Methods & statistical evidence assessment
- 5 questions a peer reviewer would ask
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Frequently asked questions
What is procedural rhetoric and how does it apply to AI chatbots?
Procedural rhetoric, a term coined by game scholar Ian Bogost, is persuasion through rule-based representations and interactions rather than words or images. This paper applies the concept to generative AI chatbots, arguing that how a chatbot structures interaction — instant conversational answers, pre-summarized information, no visible source-evaluation ritual — is itself an argument that persuades users to treat the system as a trustworthy expert.
Is this paper an empirical study of AI chatbot users?
No. It is a conceptual/position paper — an encore submission to a CSCW 2026 workshop extending the authors' 2026 IEEE Transactions on Technology and Society article. It contains no user data, experiments, or system audits; it synthesizes rhetoric, information-science, and HCI literature to offer a vocabulary for analyzing the risks of AI-mediated information seeking.
What risks of using chatbots for information seeking does the paper identify?
The authors argue that chatbots replace search-and-recall with pre-located, pre-summarized answers, which may eliminate the practice of evaluating sources that builds critical discernment, narrow users' exposure to diverse perspectives, and encourage outsourcing judgment to systems that lack genuine experience or expertise.
Do the authors claim AI chatbots can never be experts?
They argue that expertise requires skills and recognition achieved through experience and negotiation with an audience of epistemic claims (citing Ericsson and Collins & Evans), which AI systems do not engage in; chatbots may therefore display signifiers of expertise without possessing it, and their outputs should not be accepted unquestioningly.
About this review
This is an AI-generated preliminary review by Evolve.Pub, produced from the openly available text of the paper. It is not peer review and does not represent the authors' views. The canonical record of the paper is at the publisher link above.
- Review is based on the extracted text only; the parent journal article [21] was not available, so the actual degree of overlap cannot be verified beyond the authors' own description of this as an encore submission.
- As a conceptual paper, the assessment concerns argument quality and novelty, not statistical validity; no figures or tables exist to check against the extracted text.
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