The Decision to Verify: How Warmth and User Characteristics Shape Reliance on Conversational Agents for Information Search

2026-05-27

AI Summary

This study investigates how users’ verification behaviors are influenced when interacting with conversational AI alongside web search, particularly in contexts where overreliance on AI may lead to uncritical acceptance of inaccurate information. Through a mixed-subjects experiment, we examine the impact of conversational style—warm versus neutral—and the moderating role of individual differences such as digital literacy. Findings reveal that overreliance persists even when verification is feasible, with verification behavior primarily driven by users’ preexisting cognitive tendencies. A warm conversational style indirectly increases agreement with incorrect AI responses. Consulting additional AI sources improves answer accuracy, whereas traditional web search proves ineffective. The study identifies two distinct user profiles—“habitual verifiers” and “default trusters”—providing empirical foundations for designing trustworthy conversational search systems.

Abstract

Conversational artificial intelligence (AI) provides an efficient and convenient gateway to information access. However, it can cause overreliance when users blindly trust AI and accept its answers without fact-checking. Information search increasingly follows a hybrid interaction paradigm that combines conversational AI with web search, making fact-checking easier. In this paper, we examine whether this interaction paradigm is effective in curbing reliance. We further investigate the underlying factors (e.g., digital literacy and conversation warmth) that drive users to verify AI answers. We conduct a mixed-subjects question-answering experiment where participants interact with either a warm or a neutral chatbot. Our findings reveal that reliance persists despite users having access to both conversational and web search. The decision to verify is driven primarily by existing user perceptions (e.g., prior trust in chatbots) rather than answer properties, with some users fact-checking regardless of the context and others trusting chatbots by default. Warm conversational style has an indirect yet critical influence on reliance by increasing agreement with the chatbot when it is incorrect. Consulting additional AI sources predicts higher accuracy, while traditional web search does not. Our study extends overreliance research by: (a) demonstrating its persistence despite access to fact-checking, (b) identifying verification behavior as user-dependent, and (c) revealing conversational warmth's indirect effect on overreliance with implications for designing trustworthy conversational search systems.

Problem

• overreliance
• conversational AI
• fact-checking
• information search
• user verification

Innovation

• overreliance
• conversational warmth
• fact-checking behavior
• hybrid search paradigm
• user-dependent verification