Understanding Students’ Perceptions of AI-Driven Adaptive Learning in Nigerian Universities: Insights from a Multi-Institutional Study
Published in Computational Sciences, Behavioural Sciences & Psychology, and Education
This study investigated the perceptions of 552 undergraduate science students drawn from six government-owned universities in South-Western Nigeria, using a descriptive survey design and non-parametric statistical analyses.
Our findings reveal three key insights:
- Academic progression strongly shapes perception: Students at higher levels of study (particularly 400–500 level) reported significantly more positive perceptions of AI-driven adaptive learning compared to lower-level students. This suggests that academic maturity and cumulative exposure enhance students’ ability to engage with and benefit from AI-supported learning systems
- Institutional context matters more than expected: Contrary to common assumptions, students from state universities demonstrated significantly more positive perceptions than those from federal universities, with a large effect size. This highlights how institutional conditions, such as instructional practices, resource pressures, and flexibility in innovation, may influence how AI is experienced.
- Gender differences are minimal in structured AI environments: While slight variations were observed, gender did not emerge as a strong determinant of perception when AI tools are embedded within formal instructional contexts. This supports the growing argument that equitable instructional design can mitigate demographic disparities in AI engagement.
The study reinforces that students’ perceptions of AI are not determined by the technology alone, but by the interaction between learner readiness, institutional context, and pedagogical integration. For reviewers and researchers, these findings indicate the importance of evaluating AI in education beyond performance outcomes. Understanding how students experience and interpret AI systems is critical for explaining why similar technologies produce different outcomes across different institutions.
The study also contributes to ongoing discussions around equity, instructional design, and context-sensitive AI adoption in higher education, particularly within resource-constrained systems.
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