<![CDATA[Nigeria’s higher education sector faces persistent challenges, even as artificial intelligence shows growing potential to improve learning outcomes, while prior findings in the Nigerian university context remain fragmented and methodologically inconsistent. This study aimed to quantitatively synthesize empirical evidence on AI’s impact on academic achievement among Nigerian university students, identify moderating variables explaining effect heterogeneity, and document implementation challenges constraining AI adoption in the educational sector. Following PRISMA 2020 guidelines, a systematic search of eight bibliographic databases identified 47 eligible studies published between 2022 and 2025, covering a combined sample of 8,234 undergraduate and postgraduate students from federal and state universities in Nigeria. Random-effects models with restricted maximum likelihood estimation were conducted in R using the metafor package, with Hedges’ g as the primary effect size. Moderator analyses applied mixed-effects models and meta-regression across seven variables, while publication bias was examined using Egger’s regression test and trim-and-fill analysis. The pooled effect was moderate to large (g = 0.68, 95% CI [0.54, 0.82], p < .001), with substantial heterogeneity (I² = 86.5%) indicating important moderator effects. The strongest outcomes were associated with intelligent tutoring systems (g = 0.91), individualized learning strategies (g = 0.79), STEM disciplines (g = 0.84), and interventions lasting more than eight weeks (g = 0.81). Key implementation barriers included poor internet connectivity (91.5%), unreliable electricity supply (87.2%), limited faculty AI competence (89.4%), and financial constraints (85.1%). These findings support evidence-based AI integration policies in Nigerian higher education, particularly in infrastructure development, faculty training, and equitable implementation strategies.]]>
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