Editorial Note: From Conventional AI to Modern AI in Education: Re- examining AI and Analytic Techniques for Teaching and Learning

Haoran XIE, Gwo-Jen HWANG*, Tak Lam WONG

*Corresponding author for this work

Research output: Journal PublicationsEditorial/Preface (Journal)

Abstract

With the rapid development and significant successfulness of various deep learning techniques in artificial intelligence (AI) in recent years, the connotation of AI has been transformed from traditional rule-based or statistical learning models to deep learning models. Such a transformation of AI has led to a significant evolution in both academic and industrial fields. To understand the potential impact of AI evolution for future teaching and learning, it is necessary to re-examine the opportunities, research issues, and roles of AI in education as modern AI enables the possibility of playing vital roles in education, which are not only limited to intelligent tutors/tutees but also intelligent learning partners or policy making advisors. Motivated by the recent transformation and trends in AI in education, this special issue, including 12 research articles, aims to launch an in-depth discussion on re-examining AI and analytics techniques in teaching and learning applications.
Original languageEnglish
Pages (from-to)85-88
Number of pages4
JournalEducational Technology and Society
Volume24
Issue number3
Publication statusPublished - Jul 2021

Bibliographical note

Funding Information:
This study is supported in part by the Ministry of Science and Technology of Taiwan under contract numbers MOST-109-2511-H-011-002-MY3 and MOST-108-2511-H-011-005-MY3.

Publisher Copyright:
© 2021. All Rights Reserved.

Keywords

  • Modern AI
  • AI transformation
  • Deep neural networks
  • Analytic techniques
  • AI in education

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