SALEH ALKHAMEES, ALI ALFAGEEH, BADER ALKHAZI, DUAA ALSHDAIFAT, AMIN ALIPOUR
8 min
This paper fills a critical gap in AI education research by studying trust and usage of generative AI tools among computer science students in Saudi Arabia, Kuwait, and Jordan—regions largely overlooked in favor of Western (WEIRD: Western, Educated, Industrialized, Rich, Democratic) populations. Authors from the University of Houston and Kuwait University surveyed students across four universities, replicating a US study to uncover how cultural, linguistic, and educational contexts shape AI adoption. The findings reveal a 'healthy balance' where students view AI as a helpful assistant rather than an infallible expert, with 98% having tried tools like ChatGPT for programming tasks such as tutoring, debugging, and pair programming.
A standout discovery is the negative correlation between English proficiency and trust in AI. Students more fluent in English reported lower confidence in AI outputs, likely because they can better detect inaccuracies in the predominantly English-trained models. Conversely, lower English proficiency predicted higher trust—students rely more on AI when language barriers make independent verification harder. This inverts Western assumptions and highlights the need for multilingual AI tailored to Arabic-speaking users, where cultural nuances and right-to-left script add further challenges.
Challenging US trends (where females often trust AI more), Saudi female CS students showed lower trust than males. This aligns with Saudi Arabia's gender-segregated campuses, contrasting mixed-gender settings in Kuwait and Jordan, where no gender differences emerged. The paper suggests environmental factors like isolation amplify skepticism among females, urging context-specific interventions rather than universal gender stereotypes. First-generation status showed minimal impact, unlike in the US.
Usage is rampant: nearly all students (98%) have used AI for homework and learning, praising its speed for brainstorming and code generation. However, limitations in deep reasoning spark caution—AI excels at syntax but falters on complex CS problems. This raises plagiarism risks and calls for updated policies, as institutions grapple with detecting AI-assisted work. The study emphasizes AI as a 'digital peer' enhancing personalized learning, but only with safeguards.
Most HCI and AI education studies ignore the Middle East's linguistic diversity (Arabic dominance) and cultural norms, risking inequitable tech deployment. Intra-regional variations—e.g., Saudi gender gaps absent elsewhere—prove even 'similar' areas differ. Implications push for culturally attuned AI design, addressing digital divides seen in comparisons to US/Bangladeshi students (e.g., internet/cost barriers). Ultimately, understanding these patterns ensures AI benefits all students, not just WEIRD ones, promoting global equity in CS education.
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.
Alex: Huh, so better English makes them less trusting here? That seems counterintuitive—walk me through why segregation fits in.
Sam: Gender-separated classes in Saudi mean female students often have less casual exposure to tech peers or industry talks, which are male-dominated fields there. This setup, combined with language hurdles, builds extra caution—Saudi females showed notably lower trust than males, reversing the US trend where females tend to trust AI more. In contrast, mixed-gender schools in Kuwait and Jordan showed no such gap. The study replicated a US survey in Arabic to isolate this: it's the local education environment amplifying barriers, not some broad cultural trait.
Alex: So segregation amplifies those caution barriers for Saudi females. What does the data show on their confidence levels compared to males?
Sam: In Saudi classes, males reported higher confidence across key areas—like feeling able to finish assignments alone or staying motivated during coding. About twice as many males as females agreed they could solve problems independently when stuck. This gap ties directly to segregated setups, where females get less peer exposure in tech-heavy courses.
Alex: Twice as many—that's a clear difference. How did they measure this exactly?
Sam: They used a survey handed out in actual CS classes—like intro programming or data structures—at universities in Saudi Arabia, Kuwait, and Jordan. Everyone answered in Arabic, covering demographics, programming confidence, AI trust, and usage habits. Questions split students into groups: those who've used AI, heard of it but not tried, or never heard—though almost none fell into the last two.
Alex: So even with everyone trying AI, Saudi females lag in confidence partly due to less exposure. That points to real equity issues in tech education here.
Sam: Overall, students rated their trust around 3.1 out of 5 across six questions—enough to use AI as a helpful tool, but not so much they'd follow it blindly. They agreed most with knowing what AI might do next, but least with relying on its answers when unsure, with nearly half disagreeing on blind trust. The paper calls this healthy skepticism.
Alex: Balanced, got it—like treating it as a study buddy, not the teacher. But you mentioned country differences—did Saudi stand out there too?
Sam: Yes, Saudi students had the lowest average at about 2.9, while Jordan topped at 3.2 and Kuwait at 3.2. That pattern held for most questions, suggesting local factors nudge trust down in segregated settings. Notably, only in Saudi did females rate trust much lower than males—around 2.5 versus 3.4 overall—flipping US patterns where females trust more.
Alex: Lower in Saudi across the board, and the gender gap only there. What tied it to language skills specifically?
Sam: Researchers looked at how factors lined up by checking pairs—like does stronger English match higher or lower trust? In Saudi, better English linked to less confidence in AI, likely because fluent students catch AI's slip-ups in English-heavy coding docs. Trust itself tied strongly to feeling more confident, showing reliable tools build self-assurance.
Alex: So pulling it all together, this shows trust in these tools isn't some fixed thing—it's shaped by things like school setups and language comfort. What does that mean for actually using AI fairly in places like Saudi classes?
Sam: The paper suggests schools tailor support to local needs—like workshops for female students on spotting AI strengths and limits, or pairing them with peers who've used it successfully. This could close gaps in confidence from less exposure in segregated environments. Culturally, traits like avoiding uncertainty explain the caution: students treat AI as a helper needing checks, not an authority replacing teachers.
Alex: Right, like adjusting a recipe for different kitchens instead of one-size-fits-all. And those cultural bits—avoiding unknowns and respecting human experts—make the balanced trust feel sensible here.
Sam: Exactly. High use pairs with healthy doubt, rooted in power structures where instructors hold sway over new tech. The study frames this as a strength: students gain learning boosts without over-relying, as comments show—like using AI for ideas but owning the thinking.
Alex: Makes sense why positives like better knowledge stand out, but with human judgment key. Any catches in the work itself we should note?
Sam: A main limit is the small sample—202 students from just four universities—which makes it hard to apply widely across the region. Still, it isolates education's role clearly by comparing segregated and mixed setups.
Alex: Fair point on the sample; keeps expectations realistic. Overall, this fills a real gap—showing non-Western views on AI demand custom strategies for equal benefits in tech classes. Thanks for joining me, Sam. Thanks for listening to ResearchPod.