Despite over two decades of commercialization, conversational AI continues to produce functional and communicative errors. This study examines how users’ cultural and linguistic backgrounds influence their experience with conversational AI as well as their error tolerance and adoption rates. We hypothesize that users’ cultural and linguistic backgrounds affect both error tolerance and user experience and that the number of languages a user speaks may amplify this effect. To evaluate this, we conducted in-depth qualitative, 1-hour interviews with eight multilingual users based on a standardized set of questions. All sessions were conducted online on Zoom and were audio-recorded. Results revealed that multilingual users face more diverse linguistic and cultural challenges yet demonstrate greater error tolerance, often continuing to use AI for basic tasks despite inconveniences. Furthermore, monocultural and language users are more likely to discontinue use when errors persisted. By adapting to diverse users, conversational AI can enhance user experience, reduce disparities, and promote equitable access. This study provides insights for developing inclusive, sustainable, and socially responsible conversational AI systems accessible to a global user base. However, limitations include the narrow diversity of participants’ countries and languages and a small sample size. Future research should expand participant diversity to provide a more comprehensive and deeper understanding of conversational AI systems.

Abstract
1. Introduction
2. Literature Review andPrevious Research
3. Method
    3.1. Procedure
    3.2. Participants
4. Results
    4.1. Results of Interview-based User Study
5. Discussion
6. Conclusion
REFERENCES

• Sooin Cho(Doctoral Student, AI Design Lab, Dept. of Smart Experience Design, Kookmin University)
• Dayoung Ju(Professor, AI Design Lab, Dept. of Smart Experience Design, Kookmin University) Corresponding author