Proactive and Reactive Engagement of Artificial Intelligence Methods for Education: A Review

TL;DR

This review comprehensively analyzes how AI, machine learning, and deep learning are used in education, covering proactive planning and reactive knowledge delivery, highlighting research trends, challenges, and the impact of COVID-19 over two decades.

Contribution

It introduces a novel categorization of AI engagement in education, analyzing 194 research articles to reveal paradigm shifts and future directions in AI for education.

Findings

AI methods support various educational stakeholders

COVID-19 accelerated AI adoption in education

Significant evolution in data and algorithm use over two decades

Abstract

Quality education, one of the seventeen sustainable development goals (SDGs) identified by the United Nations General Assembly, stands to benefit enormously from the adoption of artificial intelligence (AI) driven tools and technologies. The concurrent boom of necessary infrastructure, digitized data and general social awareness has propelled massive research and development efforts in the artificial intelligence for education (AIEd) sector. In this review article, we investigate how artificial intelligence, machine learning and deep learning methods are being utilized to support students, educators and administrative staff. We do this through the lens of a novel categorization approach. We consider the involvement of AI-driven methods in the education process in its entirety - from students admissions, course scheduling etc. in the proactive planning phase to knowledge delivery, performance assessment etc. in the reactive execution phase. We outline and analyze the major research directions under proactive and reactive engagement of AI in education using a representative group of 194 original research articles published in the past two decades i.e., 2003 - 2022. We discuss the paradigm shifts in the solution approaches proposed, i.e., in the choice of data and algorithms used over this time. We further dive into how the COVID-19 pandemic challenged and reshaped the education landscape at the fag end of this time period. Finally, we pinpoint existing limitations in adopting artificial intelligence for education and reflect on the path forward.