Students’ perception of artificial intelligence in achieving adaptive learning in Nigerian universities

Bibliographic Data

ID22436738
AuthorsBamidele Emmanuel Tijani (0009-0009-3466-4140, University of Lagos, corresponding author), Adeniyi Michael Adeduyigbe (0009-0006-4864-2119, University of Ibadan)
Year2026
Volume5
Issue1
Publication date2026-02-24
Open AccessYes
TypeARTICLE
VenueDiscover Education (JOURNAL)
Journal identifiersISSN: 2731-5525 • E-ISSN: 2731-5525
PublisherSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s44217-026-01292-5
OpenAlexW7131252597
LanguageEN
References cited22

Artificial intelligence (AI)–driven adaptive learning is increasingly promoted as a means of personalising instruction and addressing instructional constraints in higher education. However, empirical evidence remains limited on how undergraduate science students perceive the influence of AI-powered adaptive learning, particularly within developing countries like Nigeria. This study examined science students’ perceptions of the influence of AI-driven adaptive learning across six government-owned universities in South-Western Nigeria. A descriptive survey design was employed, with data collected from 552 undergraduate science students using a structured questionnaire. Non-parametric analyses (Kruskal–Wallis H and Mann–Whitney U tests) were used to examine differences in perception by level of study, institutional type, and gender. The findings revealed significant differences in perception based on students’ level of study and university type, with higher-level students and those in state universities reporting more positive perceptions of AI-driven adaptive learning. Gender differences were not statistically significant. These findings suggest that academic progression and institutional context play a critical role in shaping students’ experiences of adaptive AI technologies. The study contributes empirical evidence on undergraduate science students’ perceptions of AI-supported adaptive learning in Nigerian universities. It further provides policy- and practice-relevant implications for curriculum design, institutional support, and targeted faculty development to support effective and equitable AI integration in science education.

Adaptive Learning · Curriculum · Empirical evidence · Empirical research · Perception · E-Learning and COVID-19 · Scientific Research and Technology · Technology-Enhanced Education Studies

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