Problem
Research questions and friction points this paper is trying to address.
Trust in AI
Middle Eastern students
Cultural differences
Language fluency
Non-WEIRD populations
Trust in AI among Middle Eastern CS Students: Investigating Students' Trust and Usage Patterns Across Saudi Arabia, Kuwait and Jordan
🤖 AI Summary
This study addresses the underrepresentation of Middle Eastern Arabic-speaking populations in AI trust research, which has predominantly relied on WEIRD (Western, Educated, Industrialized, Rich, Democratic) samples, thereby limiting the global equity of AI adoption. Conducting surveys across four universities in Saudi Arabia, Kuwait, and Jordan, this work presents the first systematic investigation of AI trust among computer science students in the region and adapts existing trust assessment frameworks to non-WEIRD contexts. Findings reveal that language fluency significantly predicts AI trust levels; female students in Saudi Arabia exhibit notably lower trust in AI than their male counterparts—a gender gap absent in other surveyed countries—and higher English proficiency correlates negatively with students’ confidence in their own abilities. These results underscore the critical role of cultural and linguistic factors in shaping AI trust and offer empirical grounding for culturally responsive AI design.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
AI trust
non-WEIRD populations
language fluency
gender differences
cross-cultural AI adoption
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