A Comparative Analysis Of AI-Powered Adaptive Learning Systems in Higher Education Across Developed Countries

Suhana Saad, Mohd Nor Shahizan Ali, Zaimah Ramli and Sarmila Md. Sum
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Suhana Saad: Centre for Research in Development, Social & Environment Faculty of Social Sciences & Humanities Universiti Kebangsaan Malaysia 43600, Bangi, Selangor, Malaysia
Mohd Nor Shahizan Ali: Centre for Research in Media & Communication Faculty of Social Sciences & Humanities Universiti Kebangsaan Malaysia 43600, Bangi, Selangor, Malaysia
Zaimah Ramli: Centre for Research in Development, Social & Environment Faculty of Social Sciences & Humanities Universiti Kebangsaan Malaysia 43600, Bangi, Selangor, Malaysia
Sarmila Md. Sum: Centre for Research in Development, Social & Environment Faculty of Social Sciences & Humanities Universiti Kebangsaan Malaysia 43600, Bangi, Selangor, Malaysia

International Journal of Research and Innovation in Social Science, 2025, vol. 9, issue 5, 5877-5888

Abstract: The integration of Artificial Intelligence (AI) into higher education is significantly transforming traditional pedagogical models, particularly through the emergence of adaptive learning systems. These AI-powered platforms enhance the learning experience by tailoring content delivery to individual student needs, adapting in real time based on performance, learning pace, and engagement. This personalized approach promotes more effective and engaging learning, addressing diverse student abilities and improving overall academic outcomes. This concept paper explores the implementation and impact of AI-driven adaptive learning in higher education across five leading nations: the United States, the United Kingdom, Australia, China, and Singapore. These countries have emerged as frontrunners in educational innovation, each leveraging AI through distinct policy frameworks, institutional strategies, and varying levels of technological maturity. By analysing their approaches, this paper seeks to uncover how universities in these regions are integrating adaptive learning systems, examining the successes they have achieved, the challenges they face, and the lessons they have learned along the way. In doing so, the paper identifies key institutional practices and national strategies that have facilitated effective adoption. It also proposes a conceptual framework to guide higher education institutions in other contexts seeking to implement similar technologies. By synthesizing global experiences and drawing meaningful comparisons, the paper aims to offer practical insights and strategic recommendations. Ultimately, it provides a roadmap for institutions aspiring to harness the power of AI to develop more personalized, responsive, and future-ready learning environments that can meet the evolving needs of students in the 21st century.

Date: 2025
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