AI technologies for education: Recent research & future directions

TLDR

The rapid advancement of AI in education necessitates critical initiatives to address ethics and privacy, and calls for interdisciplinary, large‑scale, longitudinal research and development collaborations. This article aims to comprehensively review empirical studies on AI in education from 1993 to 2020, reporting the current state, highlighting technologies and benefits, bridging gaps between innovation and application, and offering practical examples for technologists and educators. The authors selected 40 empirical studies from the Web of Sciences and AIEd journals and analyzed them using bibliometrics, content analysis, and categorical meta‑trend analysis. The review reveals the current state of AIEd research, demonstrates proven and potential benefits, bridges innovation–application gaps, and discusses practical implications and future research directions from multiple perspectives.

Abstract

From unique educational perspectives, this article reports a comprehensive review of selected empirical studies on artificial intelligence in education (AIEd) published in 1993–2020, as collected in the Web of Sciences database and selected AIEd-specialized journals. A total of 40 empirical studies met all selection criteria, and were fully reviewed using multiple methods, including selected bibliometrics, content analysis and categorical meta-trends analysis. This article reports the current state of AIEd research, highlights selected AIEd technologies and applications, reviews their proven and potential benefits for education, bridges the gaps between AI technological innovations and their educational applications, and generates practical examples and inspirations for both technological experts that create AIEd technologies and educators who spearhead AI innovations in education. It also provides rich discussions on practical implications and future research directions from multiple perspectives. The advancement of AIEd calls for critical initiatives to address AI ethics and privacy concerns, and requires interdisciplinary and transdisciplinary collaborations in large-scaled, longitudinal research and development efforts.