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

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Authors: Bamidele Emmanuel Tijani, Adeniyi Michael Adeduyigbe
Cited by: 2
References: 21

Abstract

Abstract 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.

Fields of Study & Subjects

Scientific Research and Technology E-Learning and COVID-19 Technology-Enhanced Education Studies

Publication details

Journal / source
Discover Education
Publisher
Springer Science and Business Media LLC
Volume
5
Issue
1
Publication date
24 February 2026
Language
EN