Generative AI Adoption and Perceived Academic Impact Among Undergraduate Students: A Cross-National Study in Indonesia, Tajikistan, and the United States

Generative artificial intelligence (AI) is rapidly transforming higher education, yet comparative evidence from diverse international settings remains limited. This cross-national study examined undergraduate students’ adoption and perceived academic impact of generative AI tools at institutions in Indonesia, Tajikistan, and the United States. A harmonized online survey was administered to 584 undergraduates during Spring 2026 (Indonesia n = 235; Tajikistan n = 226; United States n = 123). The survey assessed AI familiarity, adoption patterns, frequency and purpose of use, perceived academic impact, and factors encouraging AI engagement. AI adoption differed significantly across sites, with the highest adoption observed in Indonesia (84.3%), followed by Tajikistan (67.7%) and the United States (47.2%). These differences remained significant after adjustment for demographic and educational variables. Self-rated AI familiarity was the strongest predictor of both adoption and perceived academic benefit across all three settings. Differences across sites persisted even after accounting for participants’ academic discipline. The perceived-impact scale demonstrated excellent internal consistency (Cronbach’s α = 0.907). Brainstorming, writing assistance, and broader task diversification were associated with higher perceived benefit. Exploratory analyses found limited evidence linking learning-style preferences to AI use patterns. The findings highlight the importance of institutional context, self-rated AI familiarity, and curriculum integration in shaping student engagement with generative AI.