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.
Follow the Topic
-
Discover Education
An international, peer-reviewed open access journal that publishes original work in all areas of education, serving the community as a broad-scope journal for academic trends and future developments in the field.
Related Collections
With Collections, you can get published faster and increase your visibility.
Empowering Education through AI: Opportunities, Challenges and Risk Governance
Artificial Intelligence (AI) is reshaping the landscape of education as a double-edged sword. On one hand, it holds great promise for empowering teaching, learning, assessment, and educational management by making them more efficient, accurate, adaptive, and responsive. On the other hand, the increasing integration of AI in education elicits significant pedagogical, ethical, social, and policy concerns, such as reduced learner agency, algorithmic control, academic misconduct, and exacerbated educational disparities. Without proper risk governance, the AI technologies intended to empower education may end up disrupting the educational processes and wreaking chaos on educational development. This calls for coordinated efforts from policymakers, researchers, and practitioners worldwide to ensure the legitimate, appropriate, responsible, and ethical use of AI in education.
Against this backdrop, this Collection aims to promote critical inquiry into the intricate and multifaceted features of AI use in education. We particularly welcome interdisciplinary perspectives that explicate how policymakers, researchers, educators, and learners can participate in and collaborate to leverage the transformative power of AI while mitigating the potential risks within different educational scenarios and across diversified cultural contexts. Potential topics include (but are not limited to):
1. Social, ethical, cognitive, emotional, and behavioural terrains of AI use in education
2. Human-AI collaboration in teaching, learning, assessment, and educational management
3. Learner agency, self-regulation, and AI-empowered learning
4. Teacher professional development in AI-empowered education
5. AI literacy for educators and learners
6. Educational equity and academic integrity in the AI era
7. Policy innovation and risk governance for AI use in various educational settings
8. Cross-cultural perspectives on AI use and governance in education
This Collection supports and amplifies research related to SDG 4
Keywords: Artificial Intelligence (AI); Human-AI collaboration; AI-empowered education; policy innovation; risk governance; educational equity; academic integrity
Publishing Model: Open Access
Deadline: Aug 26, 2026
Advancing Health Professions Education: Innovations, Strategies, and Global Perspectives
Health Professions Education (HPE) plays an important role in preparing competent, ethical, and compassionate healthcare professionals capable of responding to the complex demands of contemporary healthcare systems. In recent years, the field of HPE has witnessed a paradigm shift towards learner-centred, competency-based, and interprofessional education models that prioritize not only clinical expertise but also communication, professionalism, teamwork, and leadership skills.
This Collection invites original research, systematic or scoping reviews, case studies and brief reports and that critically examine the evolving landscape of HPE across medicine, dentistry, nursing, pharmacy, and other allied health disciplines. Key areas of interest include curriculum design and reform, innovative teaching and assessment methods, integration of digital and blended learning technologies, interprofessional education (IPE), faculty development, educational management, student support and well-being, and strategies to promote equity, diversity, and inclusivity in HPE.
We also welcome contributions that explore challenges and best practices in clinical training, rural and underserved community placements, global health education, policy development, accreditation, and institutional leadership in health education. Studies from low- and middle-income countries are strongly encouraged to enhance global representation and to promote scholarly exchange on scalable and contextually relevant innovations.
By consolidating scholarly work in this Collection, we aim to advance the discourse on high-quality, evidence-informed HPE that supports the achievement of Sustainable Development Goals (SDG 3 and SDG 4) and ultimately contributes to improved healthcare outcomes worldwide.
Publishing Model: Open Access
Deadline: May 31, 2026
Please sign in or register for FREE
If you are a registered user on Research Communities by Springer Nature, please sign in