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All Journal Indonesian Journal of Librarianship Journal of Applied Artificial Intelligence in Education
Kayode Sunday John Dada
Federal University of Education
Computer Science & IT Education Library & Information Science Social Sciences

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Effects of Artificial Intelligence on Academic Achievement Among Nigerian University Students: A Meta-Analysis (2022–2025) Kayode Sunday John Dada
Journal of Applied Artificial Intelligence in Education Vol 2, No 1 (2026): July 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/jaaie.v2i1.359

Abstract

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 meta for 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.
Libraries as Information Tourism Environments: : Unraveling T–A–P Activity Dynamics for Experience-Driven Information Services Kayode Sunday John Dada; Romoke Opeyemi Quadir; Amina Muhammad
Indonesian Journal of Librarianship Indonesia Journal of Librarianship Vol. 7 No. 1 (2026)
Publisher : Department Library of Governance Institut of Home Affairs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33701/ijolib.v7i1.6071

Abstract

Background: Libraries face intensifying pressure to reconceptualise their services and spaces in response to shifting user behaviour and the emergent experience economy. Despite decades of scholarship on information-seeking behaviour, the literature still lacks a unified framework that simultaneously accounts for motivational types, purposive activities, and experiential place qualities in library encounters. Purpose: This paper introduces and empirically validates the Library T–A–P (Tourism Type–Activity–Place) framework, adapting Tongtep et al.’s T–A–P Triangle from special-interest tourism recommendation to theorise information-seeking behaviour dynamics within library environments. Method: A two-phase mixed-methods approach was employed: (1) a systematic conceptual analysis synthesising literature across library and information science, tourism studies, activity theory, and experience design, following Jabareen’s (2009) conceptual framework analysis methodology; and (2) a structured expert validation survey (n = 186 library and information professionals across 12 countries), using a 5-point Likert-scale instrument with T–A–P Coherence Scoring. Inter-rater reliability for data coding was assessed via Cohen’s kappa (κ = 0.81). Result: Eight Information Tourism Types were mapped onto a taxonomy of information activities and library place typologies. Validation confirmed strong T–A–P coherence across all categories (Grand Mean = 4.38/5.00), with Cultural Heritage Tourism (4.77) and Special-Interest Tourism (4.63) yielding highest coherence indices.  Conclusion: The Library T–A–P framework offers a theoretically grounded, triadic, activity-centred paradigm for library service design, space planning, and personalised information recommendation. While expert validation provides the framework's foundational construct validity, future empirical user studies are crucial for demand-side validation.
Co-Authors Amina Muhammad Romoke Opeyemi Quadir