Digital Device Access as a Predictor of Artificial Intelligence Tool Adoption Among University Students in Uganda
Abstract
The adoption of Artificial Intelligence (AI) tools has transformed teaching and learning in higher education by supporting academic writing, programming, research, and problem-solving. However, limited empirical evidence exists on whether students' access to digital devices is associated with the adoption of these technologies, particularly in developing countries. This study examined whether digital device access predicts AI tool adoption among university students in Uganda. A quantitative cross-sectional research design was employed using a structured online questionnaire completed by 128 university students recruited through convenience and participant-referral sampling. AI tool adoption was measured using seven items covering the frequency and purpose of AI usage. Descriptive statistics, reliability analysis, one-way ANOVA, Welch's ANOVA, Kruskal–Wallis tests, post-hoc comparisons, and robust multiple regression analysis were conducted using Python. The AI tool adoption scale demonstrated good internal consistency (Cronbach's α = 0.884). Overall, 66.4% of the respondents exhibited high levels of AI tool adoption (M = 3.57, SD = 0.87). Significant differences in AI tool adoption were observed across device ownership categories (Welch's F = 6.435, p = 0.003). Students owning both a laptop and smartphone (M = 3.81) and those owning personal laptops (M = 3.63) reported significantly higher AI tool adoption than students relying solely on smartphones (M = 3.03). Robust regression analysis further revealed that device ownership significantly predicted AI tool adoption (R² = 0.112, p = 0.003), whereas internet access did not make a significant additional contribution after accounting for device ownership. The findings suggest that access to laptop-based computing environments was more strongly associated with AI tool adoption than reported internet availability within the study sample among university students. The study recommends that higher education institutions and policymakers strengthen students' access to appropriate digital devices while promoting equitable and responsible adoption of AI technologies for learning.