The purpose of this study is to examine the overlap between bilingualism and artificial intelligence with a specific focus on Arabic speakers. The paper focuses on the challenges that AI systems encounter when processing various Arabic dialects and the notion of code-switching common for most bilingual speakers. The work is based on a qualitative methodology that investigates the performance of AI tools based on semi-structured interviews with 30 bilingual learners and educators and a series of focus group meetings. The results of the study show that AI presents moderate performance in terms of handling Modern Standard Arabic, encountering severe difficulties with online code-switching and regional dialects. As such, about 60% of the study participants report that AI struggled with different dialects, and 70% show that it fought with the mixture of various languages present in the context of code-switching. Nevertheless, many participants agree that AI can be used to save the context of the Middle East and the region’s heritage and speculated about the ability of AI-assisted digitalization to ensure the access to rare and complicated texts. The study concludes in recommendations to revise the current format of AI to adjust it for the bicommunal environment and accommodate different types of Arabic dialects. Many participants agree with the necessity to use AI to preserve the Arab heritage; however, the systems must be organized and introduced in such a way as to promote social learning and better access to education.