This paper explores how cultural models of the self and environment influence people's preferences for AI. The authors conducted two survey studies with participants from European American, African American, and Chinese cultural backgrounds. The findings suggest that people from different cultures have distinct preferences for how AI should interact with them. European Americans tended to prefer AI with less autonomy and more control, while Chinese participants valued AI that could connect with them and influence their environment. African Americans showed preferences that were a blend of both models. The study highlights the importance of considering cultural differences in AI development to create more relevant and inclusive technologies. The research also emphasizes the need for culturally responsive AI that can better serve diverse populations. The authors argue that current AI theories and designs often overlook cultural perspectives, limiting the potential of AI technologies. By incorporating cultural models into AI research, the field can better understand and meet the needs of a broader range of users. The study contributes to the field of human-computer interaction by providing a framework for examining culturally shaped preferences for AI and by highlighting the implicit cultural assumptions that shape current AI design practices. The findings suggest that AI should be designed to accommodate different cultural models of self and environment, leading to more inclusive and effective technologies. The research also underscores the importance of considering cultural diversity in AI development to ensure that AI systems are relevant and beneficial to a wider segment of the global population.This paper explores how cultural models of the self and environment influence people's preferences for AI. The authors conducted two survey studies with participants from European American, African American, and Chinese cultural backgrounds. The findings suggest that people from different cultures have distinct preferences for how AI should interact with them. European Americans tended to prefer AI with less autonomy and more control, while Chinese participants valued AI that could connect with them and influence their environment. African Americans showed preferences that were a blend of both models. The study highlights the importance of considering cultural differences in AI development to create more relevant and inclusive technologies. The research also emphasizes the need for culturally responsive AI that can better serve diverse populations. The authors argue that current AI theories and designs often overlook cultural perspectives, limiting the potential of AI technologies. By incorporating cultural models into AI research, the field can better understand and meet the needs of a broader range of users. The study contributes to the field of human-computer interaction by providing a framework for examining culturally shaped preferences for AI and by highlighting the implicit cultural assumptions that shape current AI design practices. The findings suggest that AI should be designed to accommodate different cultural models of self and environment, leading to more inclusive and effective technologies. The research also underscores the importance of considering cultural diversity in AI development to ensure that AI systems are relevant and beneficial to a wider segment of the global population.