Artificial Intelligence and Underfunded Education

Kiarie Muranga; Ian S. Muse; Emin N. Köroğlu; Yetkin Yildirim

doi:10.31039/ljss.2023.6.105

Authors

Kiarie Muranga Light Academy
Ian S. Muse Light Academy
Emin N. Köroğlu The University of Texas at Austin
Yetkin Yildirim Rice University

DOI:

https://doi.org/10.31039/ljss.2023.6.105

Keywords:

underfunded education, rural education, artificial intelligence education, AI education, personalised learning, intelligent tutoring system, large classes, student-teacher ratio, electric access

Abstract

There has been a lot of general research on education, but the unique topic of underfunded education has been largely untouched. Millions of students around the world get a low-quality education due to problems in underfunded education such as the lack of experienced teachers, a high student-teacher ratio and outdated curricula. This negatively impacts them and their respective countries in the future due to subpar performance both in school and in the workplace; further exacerbating the educational and economic disparities between countries. The lack of research in this area is due to the various unique challenges facing underfunded education and the difficulty in solving them with conventional methods. However, many tools that utilise Artificial Intelligence (AI) such as adaptive learning systems, chatbots, virtual assistants and Intelligent Assessment Systems can be used to solve or at least manage these problems, making the process of education more streamlined, efficient and effective. The purpose of this study is to explore the possibility of implementing AI into underfunded education and the numerous benefits that doing so would bring. We also outline the various challenges that would be faced in implementation and possible solutions to them.

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