Transforming History Learning and Poverty Reduction with AI

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Authors: Lee Bih Ni
Cited by: 0

Abstract

This study explores the application of AI-powered educational approaches to enhance history learning and reduce poverty in three distinct Malaysian settings: the rural districts of Kota Belud and Kota Marudu, and the urban center of Kota Kinabalu. Employing a mixed-method design, the research integrates quantitative data from student performance metrics and access to AI tools with qualitative insights from educators and community stakeholders. Findings reveal that AI-enhanced platforms significantly improve student engagement, historical knowledge retention, and digital literacy, particularly when supported by localized content and teacher training. Moreover, the study highlights the potential of AI in education not only to address learning disparities but also to empower economically disadvantaged communities by fostering critical thinking, historical awareness, and future-ready skills. These insights provide valuable implications for policymakers and educational planners aiming to implement inclusive, technology-driven strategies in both rural and urban settings.

Fields of Study & Subjects

Artificial Intelligence in Education Educational Technology and Pedagogy Educator Training and Historical Pedagogy

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
4 February 2026