The integration of Artificial Intelligence (AI) into higher education curricula has gained considerable momentum globally, yet empirical evidence from Sub-Saharan Africa—particularly Uganda—remains sparse. This mixedmethods study investigated learner perceptions and appreciation of AI education delivery in selected Ugandan higher education institutions. Guided by a pragmatist epistemological framework, the study employed a concurrent triangulation design, collecting quantitative data from 312 undergraduate and postgraduate students across four universities using a structured 28-item Likert-scale questionnaire, and qualitative insights from 24 purposively selected participants through in-depth semi-structured interviews. Quantitative analysis encompassed univariate descriptive statistics, bivariate Pearson correlation, confirmatory factor analysis (CFA), and principal component analysis (PCA). Findings revealed that learners held moderately positive perceptions of AI education delivery (M = 3.76, SD = 0.58), with AI content relevance (M = 3.87) and tool usability (M = 3.72) emerging as the strongest appreciation drivers. Technology accessibility recorded the lowest mean score (M = 2.93), underscoring persistent infrastructural constraints. Bivariate analysis revealed statistically significant positive correlations between prior AI exposure and overall appreciation (r = 0.613, p < .001). Factor analysis extracted three latent constructs—AI Engagement, Infrastructure Readiness, and Pedagogical Quality—collectively explaining 65.3% of total variance. PCA confirmed that AI engagement indicators were the primary contributors to composite learner appreciation scores. Qualitative themes corroborated quantitative patterns, highlighting enthusiasm tempered by infrastructural deficits, inconsistent instructor AI competence, and limited institutional support. The study concludes that while Ugandan learners are broadly receptive to AI education, sustainable appreciation requires targeted investment in digital infrastructure, systematic instructor capacity-building, and context-sensitive AI curriculum design. Recommendations are provided for policymakers, university administrators, and curriculum developers.