Data-Driven Artificial Intelligence in Education: A Comprehensive Review

Abstract

Motivated by the importance of education in an individual's and a society's development, researchers have been exploring the use of Artificial Intelligence (AI) in the domain and have come up with myriad potential applications. This paper pays particular attention to this issue by highlighting the future scope and market opportunities for AI in education, the existing tools and applications deployed in several applications of AI in education, research trends, current limitations and pitfalls of AI in education. In particular, the paper reviews the various applications of AI in education including student grading and evaluations, students' retention and drop out prediction, sentiment analysis, intelligent tutoring, classrooms' monitoring and recommendation systems. The paper also provides a detailed bibliometric analysis to highlight the research trends in the domain over six years (2014--2019). For this study, we analyze research publications in various related sub-domains such as learning analytics, educational data mining (EDM), and big data in education. The paper analyzes educational applications from different perspectives. On the one hand, it provides a detailed description of the tools and platforms developed as the outcome of the research work achieved in these applications. On the other side, it identifies the potential challenges, current limitations and hints for further improvement. We also provide important insights into the use and pitfalls of AI in education. We believe such rigorous analysis will provide a baseline for future research in the domain.

Artificial Intelligence and Big Data: The Advent of New Pedagogy in the Adaptive E-Learning System in the Higher Educational Institutions of Saudi Arabia

“…Commercialization of educational instruments and systems has its own distinctive challenges, as do the academic and scientific aspects of AI and big data in education [22]. A number of countries have endeavoured to stimulate development found on innovation by using intensified technology transmission and encouraging "academia-industry" alliances [23].…”

Reinvigorating the Discourse on Human-Centered Artificial Intelligence in Educational Technologies

“…Current AI applications in education Ahmad et al (2020) presented a bibliometric analysis of AI applications in education. The authors divided the field of AI applications in education into ITS, evaluation, personalized learning, recommender systems, student performance, sentiment analysis, retention and dropout, and classroom monitoring.…”

“…This type of AI demands computational and judgmental processes that can be explained and interpreted, as well as continuous adaptations of AI algorithms based on human contexts and social phenomena. We used these two perspectives -AI under human control and AI in the human condition -as a starting point for structuring AI applications for education according to HCAI (Ahmad et al, 2020). Furthermore, following Dubey et al (2020), we interpreted the interactions between humans and AI as "teaming".…”

“…We applied a teaming model to structure and address relevant aspects of HCAI and adapt them to the context of educational processes (Figure 4). To represent the task characteristics of AI in this specific context, we followed Ahmad et al (2020), and proposed assigning the AI applications -ITS, evaluation, personalized learning, sentiment analysis, student performance, recommender systems, retention and dropout, and classroom monitoring -depending on characteristics of the other components of Dubey et al's (2020) framework. Figure 4 illustrates our approach using two applications as examples, which we considered to have opposite characteristics: ITS and classroom monitoring.…”

“…André Renz, Gergana Vladova extended period to analyze teachers' teaching methods and students' learning experiences (Raykov et al 2016;Ahmad et al, 2020).…”