Rethinking University Education in the Age of Artificial Intelligence: From Knowledge Transmission to Human-Centered Learning

The rapid advancement of artificial intelligence—particularly generative AI systems—is fundamentally challenging the knowledge transmission model that has defined university education for centuries. This paper examines how AI technologies are catalyzing a paradigm shift toward human-centered learning in higher education institutions worldwide. Through a comprehensive theoretical and empirical literature review spanning 2019 to 2025, we analyze three critical dimensions of this transformation: pedagogical restructuring that repositions faculty from content deliverers to learning facilitators and ethical stewards; assessment redesign strategies that move beyond policing AI use toward authentic, process-focused evaluation frameworks; and equity implications for Global South universities facing simultaneous infrastructure deficits and leapfrogging opportunities. Drawing on constructivist learning theory, human capital theory, and sociotechnical systems perspectives, we synthesize evidence from design-based research, institutional case studies, and multi-country investigations across Ghana, South Africa, Nigeria, and Ecuador. Key findings reveal that successful AI integration demands staged institutional roadmaps combining adaptive learning platforms, AI assessment literacy for instructors, curriculum modules embedding AI literacy as a core competency, and robust governance frameworks addressing bias, hallucination risks, and algorithmic accountability. For Global South contexts, targeted investments in cloud-based infrastructure, faculty capacity building, and locally relevant AI applications can enable educational leapfrogging despite resource constraints. We conclude that the future university must embrace human-centered learning as its organizing principle—cultivating critical thinking, ethical reasoning, creativity, and socio-emotional intelligence that complement rather than compete with AI systems. This transformation demands coordinated action across institutional, policy, and pedagogical domains to ensure equitable access to AI-augmented education while preserving academic integrity and epistemic rigor.