AI Should Be More Human, Not More Complex

TL;DR

This study shows that concise, transparent AI responses are preferred by users over complex, verbose answers, emphasizing the importance of human-like communication for better engagement and trust.

Contribution

The paper provides empirical evidence that simpler, source-attributed AI responses outperform complex explanations in user satisfaction and trust.

Findings

Users prefer concise, sourced responses over elaborate explanations.

Complex AI responses can reduce trust and increase cognitive load.

Human-like brevity improves user engagement with AI systems.

Abstract

Large Language Models (LLMs) in search applications increasingly prioritize verbose, lexically complex responses that paradoxically reduce user satisfaction and engagement. Through a comprehensive study of 10.000 (est.) participants comparing responses from five major AI-powered search systems, we demonstrate that users overwhelmingly prefer concise, source-attributed responses over elaborate explanations. Our analysis reveals that current AI development trends toward "artificial sophistication" create an uncanny valley effect where systems sound knowledgeable but lack genuine critical thinking, leading to reduced trust and increased cognitive load. We present evidence that optimal AI communication mirrors effective human discourse: direct, properly sourced, and honest about limitations. Our findings challenge the prevailing assumption that more complex AI responses indicate better performance, instead suggesting that human-like brevity and transparency are key to user engagement and system reliability.