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arxiv: 2605.14849 · v1 · pith:35MPBQPZnew · submitted 2026-05-14 · 💻 cs.HC

Beliefs and Misconceptions around Integrated Conversational AI

William Seymour , Adam Jenkins , Mark Cote , Jose Such This is my paper

Pith reviewed 2026-06-30 20:11 UTC · model grok-4.3

classification 💻 cs.HC
keywords conversational AIuser trustcitationsfact-checkingintegrated AIprompting strategiesLLM perceptionsbrowser extension
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The pith

Citations in integrated conversational AI raise perceived trustworthiness without prompting users to verify the sources.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper investigates how users build understanding and decide on trust when conversational AI is embedded directly into a web browser. In a study of 20 participants completing information retrieval and planning tasks with Copilot in Microsoft Edge, users combined prior views of LLMs with search engine habits to shape their prompting and evaluation. A central finding is that visible citations made answers seem more reliable, yet participants rarely felt compelled to inspect those citations themselves. When they did check facts, they frequently turned to the exact sources the AI had already referenced. This pattern shows how integration can quietly align user verification behavior with the system's own outputs.

Core claim

Participants relied on a combination of existing perceptions of LLMs and internet search, tracing the effect of beliefs about how Copilot generated answers on prompting strategies. The inclusion of citations increased the trustworthiness of answers without participants feeling the need to check them, with participants often reaching for the same information sources as the CAI when fact-checking.

What carries the argument

The controlled user study of 20 participants performing information-retrieval and planning tasks inside a browser extension, which links beliefs about answer generation to prompting choices and citation-driven trust.

If this is right

  • Including citations in AI responses can raise user acceptance of outputs even when verification does not occur.
  • Users may mirror the source selection of the integrated AI during their own fact-checking.
  • Pre-existing beliefs about how LLMs work directly shape how people phrase prompts to the system.
  • Trust mechanisms in integrated AI rest on surface markers such as citations rather than on active source inspection.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Designers of other embedded AI tools may see similar citation effects if the integration hides the AI's origins as effectively as a browser extension does.
  • Over-reliance on AI-listed sources could narrow the range of information people encounter when double-checking answers.
  • Future interfaces might need explicit prompts or training to encourage verification outside the AI's cited set.
  • The pattern could intensify as conversational features spread across more productivity applications beyond browsers.

Load-bearing premise

Observations from 20 participants in controlled tasks inside one browser extension will generalize to everyday real-world use of integrated conversational AI.

What would settle it

A follow-up observation in which users in uncontrolled settings independently verify citations or select different sources than those listed by the AI would undermine the reported trust and fact-checking pattern.

read the original abstract

LLM-driven conversational AI is beginning to disappear into the background, shifting from something used directly towards something increasingly integrated into existing workflows. In the process, markers of origin and training are smoothed away as LLMs become commodified in the eyes of users. We explore how people approach using a web browser with conversational AI built in, focusing on how they develop their understanding and determine whether to trust its outputs. We conducted a study where 20 participants used the Copilot AI features in Microsoft Edge to conduct information retrieval and planning tasks. Participants relied on a combination of existing perceptions of LLMs and internet search, tracing the effect of beliefs about how Copilot generated answers on prompting strategies. The inclusion of citations increased the trustworthiness of answers without participants feeling the need to be check them, with participants often reaching for the same information sources as the CAI when fact-checking.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper reports a qualitative study in which 20 participants performed information-retrieval and planning tasks inside the Microsoft Edge browser using its integrated Copilot feature. It claims that participants drew on prior beliefs about LLMs and web search to shape their prompting, that the presence of citations increased perceived trustworthiness without prompting verification, and that participants tended to consult the same external sources that Copilot itself referenced when they did check facts.

Significance. If the reported patterns hold beyond the specific experimental context, the work would supply useful empirical grounding for how trust and verification behaviors emerge when conversational AI is embedded in everyday tools rather than used as a standalone interface. The study design itself, however, supplies no evidence that the observed effects are stable properties of integrated CAI rather than artifacts of the single-extension, short-session, controlled-task setting.

major comments (2)
  1. [Methods / Results] The central claims about citation effects on trustworthiness and source-matching in fact-checking rest on an analysis whose protocol, coding scheme, and reliability metrics are not described. The abstract states the findings but the Methods and Results sections supply no detail on interview protocol, coding scheme, inter-rater reliability, or limitations; without these the reader cannot assess whether the reported patterns are reproducible or whether they are shaped by the particular task framing.
  2. [Discussion / Limitations] The generalizability concern is load-bearing: the study uses a single browser extension, 20 participants, and controlled information-retrieval/planning tasks. The manuscript does not discuss how the novelty of the tool, the short session length, or the participant pool might have produced prompting and verification habits that would not appear in everyday, multi-tool, long-term use of integrated conversational AI.
minor comments (1)
  1. [Introduction] The abstract and introduction use the term 'integrated conversational AI' without a precise operational definition or comparison to standalone chat interfaces; a short clarifying paragraph would help readers map the claims onto existing literature.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for the opportunity to respond to the referee's comments. We find the feedback constructive and will revise the manuscript to address the concerns raised regarding methodological transparency and generalizability.

read point-by-point responses

  1. Referee: [Methods / Results] The central claims about citation effects on trustworthiness and source-matching in fact-checking rest on an analysis whose protocol, coding scheme, and reliability metrics are not described. The abstract states the findings but the Methods and Results sections supply no detail on interview protocol, coding scheme, inter-rater reliability, or limitations; without these the reader cannot assess whether the reported patterns are reproducible or whether they are shaped by the particular task framing.

    Authors: We agree with this assessment. The initial manuscript did not provide adequate detail on the qualitative analysis process. In the revised version, we will include a detailed description of the interview protocol (including the semi-structured questions used), the inductive thematic analysis approach, the coding scheme with examples, and any steps taken for reliability such as multiple coders reviewing transcripts. We will also explicitly discuss limitations related to task framing in a new Limitations section. This will allow readers to better evaluate the reproducibility and context of our findings. revision: yes

  2. Referee: [Discussion / Limitations] The generalizability concern is load-bearing: the study uses a single browser extension, 20 participants, and controlled information-retrieval/planning tasks. The manuscript does not discuss how the novelty of the tool, the short session length, or the participant pool might have produced prompting and verification habits that would not appear in everyday, multi-tool, long-term use of integrated conversational AI.

    Authors: We acknowledge that the manuscript's limitations section is underdeveloped on these points. While our study is positioned as an initial exploration of integrated CAI use, we will expand the Discussion to address how the specific context (single tool, short sessions, controlled tasks, participant demographics) may influence the observed behaviors. We will discuss potential novelty effects, the difference between lab-like sessions and naturalistic long-term use, and suggest directions for future research to test generalizability across tools and over time. This revision will better frame the scope of our claims without overstating them. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical qualitative study with direct observations

full rationale

The paper reports results from a controlled user study with 20 participants performing information-retrieval and planning tasks in Microsoft Edge Copilot. No equations, fitted parameters, derivations, or mathematical predictions appear in the abstract or described content. Central claims rest on direct participant observations rather than any self-referential reduction, self-citation chain, or renamed known result. The analysis is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The study rests on standard HCI assumptions about the validity of think-aloud and interview data for inferring beliefs; no free parameters or invented entities are introduced.

axioms (1)
  • domain assumption Qualitative analysis of participant behavior and statements can reliably reveal mental models of AI systems
    Invoked implicitly by the choice to conduct and interpret a user study

pith-pipeline@v0.9.1-grok · 5672 in / 1069 out tokens · 24174 ms · 2026-06-30T20:11:53.511698+00:00 · methodology

discussion (0)

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Reference graph

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