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