Effects of Artificial Intelligence on Academic Performance of Library and Information Science University Students: A Meta-Analysis (2023-2025)

Library and Information Science (LIS) education in Nigeria confronts persistent structural challenges, while artificial intelligence presents expanding opportunities for enhancing student learning outcomes. Prior research on Artificial Intelligence (AI’s) impact in Nigerian LIS programs remains methodologically inconsistent and discipline-specific syntheses are absent. This study aimed to quantitatively synthesize empirical evidence on AI’s impact on academic performance among Nigerian LIS university students across undergraduate and postgraduate levels-including master of Library and Information Science (Master of Library and Information Science), Master of Information Science, Master of Library Science (MLS), Doctor of Philosophy in Library and Information Science (PhD LIS), Doctor of Philosophy in Information Science (PhD IS), and Doctor of Philosophy in Library Science (PhD LS) programs-identify moderating variables, and document implementation challenges constraining AI adoption in Nigerian Library Schools. Following PRISMA 2020 guidelines, a systematic search of eight bibliographic databases identified 42 eligible studies published between January 2023 and December 2025, covering a combined sample of 6,847 undergraduate and postgraduate LIS students from federal and state universities across 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. The pooled effect was moderate (g=0.61, 95% CI [0.47, 0.75], p<0.001), with substantial heterogeneity (I²=85.2%) indicating important moderator effects. The strongest outcomes were associated with intelligent tutoring systems (g=0.84), individualized learning strategies (g=0.72), information technology subject areas (g=0.76), and interventions lasting more than 8 weeks (g=0.73). Critical implementation barriers included limited faculty AI competencies (92.9%), inadequate Internet connectivity (95.2%), unreliable electricity supply (90.5%), LIS curriculum integration gaps (73.8%), and data security concerns (76.2%). These findings support evidence-based AI integration policies in Nigerian LIS education, particularly targeting infrastructure development, faculty training, and LIS-specific curriculum redesign.