About the Author:
Dr Alvin Chan is the Yvon Pfeifer Professor of Artificial Intelligence & Emerging Technologies
at Cambridge Corporate University (Switzerland), specialising in AI and educational innovation.
He has led teacher training in digital pedagogy and generative AI, developed AI-powered
educational applications, and pioneered the integration of Multiple Intelligence frameworks. Dr
Chan has held academic leadership roles, serves on editorial boards, and is a peer reviewer for
leading journals in artificial intelligence. His work centres on scalable, inclusive AI solutions for
teaching and learning.
Abstract
The COVID-19 pandemic exposed and deepened educational inequities across Asia, particularly
in developing countries where digital divides, infrastructural limitations, and pedagogical gaps
threatened learning continuity. The author’s self-developed KAMI framework—Knowledge,
Assessment, Mentorship, Interests—emerged as a holistic, adaptable approach to teacher training
and e-learning, evolving into eKAMI to meet the demands of digital education. This paper
presents a comprehensive, AI-infused eKAMI model, leveraging free and accessible artificial
intelligence applications to enhance e-learning for educators, students, and educational
technologists in resource-constrained environments. Through an extensive literature review,
theoretical analysis, and case studies from Indonesia and India, the author demonstrates how
eKAMI provides scalable, affordable, and contextually relevant e-learning solutions during and
beyond the pandemic. Policy implications, implementation strategies, and future research
directions are discussed to offer a robust roadmap for sustainable, inclusive AI-enhanced digital
education in Asia.
Keywords: AI-enhanced e-learning, digital divide, KAMI framework, teacher training, resource-
constrained environments, educational equity