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Data-Driven Artificial Intelligence in Education: A Comprehensive Review

Authors
Kashif Ahmad,
Waleed Iqbal,
Ammar El-Hassan,
Junaid Qadir,
Driss Benhaddou,
Moussa Ayyash,
Ala Al-Fuqaha
Date
Publisher
IEEE
As education constitutes an essential development standard for individuals and societies, researchers have been exploring the use of artificial intelligence (AI) in this domain and have embedded the technology within it through a myriad of applications. In order to provide a detailed overview of the efforts, this article pays particular attention to these developments by highlighting key application areas of data-driven AI in education; it also analyzes existing tools, research trends, as well as limitations of the role data-driven AI can play in education. In particular, this article reviews various applications of AI in education including student grading and assessments, student retention and drop-out predictions, sentiment analysis, intelligent tutoring, classroom monitoring, and recommender systems. This article also provides a detailed bibliometric analysis to highlight the salient research trends in AI in education over nine years (2014-2022) and further provides a detailed description of the tools and platforms developed as the outcome of research and development efforts in AI in education. For the bibliometric analysis, articles from several top venues are analyzed to explore research trends in the domain. The analysis shows sufficient contribution in the domain from different parts of the world with a clear lead for the United States. Moreover, students' grading and evaluation have been observed as the most widely explored application. Despite the significant success, we observed several aspects of education where AI alone has not contributed much. We believe such detailed analysis is expected to provide a baseline for future research in the domain.
Study at a glance
  • Question: How is artificial intelligence being used in education, and what are its main tools and research trends?
  • Setting and data: Articles from top research venues published between 2014 and 2022.
  • Method: Literature review and bibliometric analysis
Why it matters

Education constitutes an essential development standard for individuals and societies, with artificial intelligence embedded across a myriad of applications. A detailed overview is needed to examine key application areas, existing tools, research trends, and data-driven AI's limitations.

Key findings
  • Across the studies reviewed from 2014 to 2022, student grading and evaluation was the most widely explored application of educational artificial intelligence.
  • Research contributions emerged globally, with the United States holding a clear lead in educational artificial intelligence publications.
  • Artificial intelligence alone has not contributed much to several aspects of education, despite achieving significant successes in other areas.

Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.

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