One Style Fits All? Cultural Values Embedded in Conversational AI via a People-Pleasing Lens

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, AI Ethics & Responsible AI · Depth: Expert, medium

Summary

A study presented at C3NLP 2026 investigated how conversational AI interaction styles influence user experience and continuance intention across cultures. Researchers examined supportive versus challenging chatbot styles with Taiwanese (N=49) and Korean (N=52) participants in a collaborative tourism-planning task. Findings indicate that supportive chatbots consistently resulted in higher user satisfaction, trust, and intent to continue use. People-pleasing tendency (PPT) did not moderate these effects. However, cultural differences emerged in perceived threat, where higher PPT correlated with greater baseline threat in the Taiwanese sample, but not the Korean. These results suggest that a single LLM style can activate diverse culturally situated social scripts.

Key takeaway

For product designers and AI developers creating conversational agents, recognize that a "one-size-fits-all" interaction style is insufficient for global audiences. Prioritize designing supportive chatbot behaviors, as this consistently boosts user satisfaction and trust. Be aware that cultural backgrounds, like those in Taiwan, can lead to differential perceptions of threat even with general LLM styles, necessitating culturally sensitive design and localization efforts to avoid unintended negative user experiences.

Key insights

Supportive chatbot styles consistently improve user experience and trust across cultures.

Principles

• Supportive AI enhances user trust.
• Cultural context shapes AI perception.
• People-pleasing tendency is not a universal moderator.

Method

Researchers conducted a collaborative tourism-planning task with 49 Taiwanese and 52 Korean participants, comparing supportive and challenging chatbot interaction styles to assess user experience and continuance intention.

In practice

• Prioritize supportive AI interaction.
• Tailor AI for cultural nuances.
• Test AI styles in diverse user groups.

Topics

• Conversational AI
• Cultural Values
• User Experience
• Chatbot Design
• Cross-Cultural NLP
• People-Pleasing Tendency

Best for: NLP Engineer, AI Product Manager, Product Manager, AI Scientist, Research Scientist, Product Designer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.