Trust in AI among Middle Eastern CS Students: Investigating Students' Trust and Usage Patterns Across Saudi Arabia, Kuwait and Jordan

Background and Context: Artificial intelligence (AI) tools have been reshaping computing and computer science education. Trust in AI is a determining factor in the adoption of these tools. Recent studies have shown different trust factors across gender and first-generation status among students. However, these studies have focused mainly on Western, Educated, Industrialized, Rich, and Democratic (WEIRD) populations, and their generalizability to other populations with different languages and cultures is unclear. Objective: This study aims to evaluate trust in AI among Middle Eastern computer science students and the factors that can impact it. Method. We replicate a recent study of trust in four universities in three Middle Eastern, Arabic-speaking countries: Saudi Arabia, Kuwait, and Jordan. We analyze trust among students across different factors such as gender and first-generation status. Findings: Our results suggest that language fluency can predict trust in AI. Moreover, unlike the results from the US population where female students tended to trust AI more than their male peers, female students in Saudi Arabia indicated lower trust compared to their male counterparts, and we did not observe any noticeable differences across gender in the other countries. We also found a generally negative correlation between English language proficiency and students' confidence. Implications: This study highlights differences in students' adoption and trust in AI even within the same region. It emphasizes the need for more investigation into students' adoption and interaction in non-WEIRD regions for equitable adoption of this technology. It also suggests a need for efforts in designing effective AI systems tailored to the cultural and linguistic needs of the region.