REVOLUTIONIZING HIGHER EDUCATION: A CROSS-SECTIONAL STUDY ON AI-POWERED SMART UNIVERSITIES FOR THE NEXT GENERATION.

Sibonelo Thanda Mbanjwa

doi:10.51168/sjhrafrica.v6i3.1632

Authors

Sibonelo Thanda Mbanjwa Mangosuthu University of Technology P.O. Box 12363 Jacobs 4026 Durban, South Africa

DOI:

https://doi.org/10.51168/sjhrafrica.v6i3.1632

Keywords:

AI in education, Smart universities, AI-powered learning, Adaptive learning environments, Higher education technology, Student engagement, Digital transformation

Abstract

Background

The integration of Artificial Intelligence (AI) in higher education has transformed teaching, learning, and administration, leading to the rise of smart universities. AI-powered tools enhance student engagement, knowledge retention, and administrative efficiency, offering personalized learning experiences and streamlining workflows. However, institutions face challenges related to faculty adaptation, ethical concerns, and data privacy risks. This study assesses the impact of AI adoption on student engagement, academic performance, and institutional challenges in higher education.

Methods

This cross-sectional quantitative study utilized structured surveys to assess AI awareness, perceived benefits, adoption levels, and challenges among 350 participants at Mangosuthu University of Technology (MUT), comprising 313 students and 37 lecturers. The collected data were analyzed using descriptive statistical methods, including mean percentages and frequency distributions, to identify key trends in AI adoption and its impact on student engagement, usage of AI tools, and academic outcomes.

Results

AI-powered learning tools significantly enhance student engagement (80%) and knowledge retention (75%), demonstrating their effectiveness in academic improvement. AI also increases administrative efficiency (70%) by automating enrolment, grading, and scheduling, reducing faculty workload. However, faculty adaptation (50%) remains a challenge due to limited training. Ethical concerns (40%), particularly regarding data privacy and algorithmic bias, necessitate greater transparency and oversight. The study found lecture capture systems (85%) and personalized content delivery (78%) to be the most widely used AI tools. Ethical dilemmas (80%), data privacy concerns (75%), and faculty resistance (60%) are key barriers to AI adoption. Additionally, a lack of resources (50%) limits access to AI-driven educational technologies.

Conclusion and Recommendations

While AI enhances student learning and institutional efficiency, faculty readiness, ethics, and infrastructure gaps remain challenges. Institutions must prioritize AI training, ethical policies, and infrastructure investment to ensure sustainable AI adoption. Encouraging faculty engagement, policy development, and continuous monitoring will maximize AI’s benefits and future-proof education.

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