There have been various definitions of the term artificial intelligence (AI) in the community of computer science. Different from “human intelligence,” AI refers to “computers that mimic cognitive functions that humans associate with the human mind, such as learning and problem-solving” (Russell, & Norvig, 2009, p. 2). Russell and Norvig (2009) argued that AI could be defined from the perspective of the intelligent agent, which can perceive the percepts from the external environment and take actions through the effectors to adapt to the environment changes or achieve certain goals. Moreover, Poole and Mackworth (2010, p.1) defined AI as “a system that acts intelligently: What it does is appropriate for its circumstances and its goal, it is flexible to changing environments and changing goals, it learns from experience, and it makes appropriate choices given perceptual limitations and finite computation.”

Although AI is not a new term, the meaning of modern AI has been changed compared to conventional AI techniques. Recently, modern AI is normally referred to the Deep Neural Networks (DNN) based techniques in recent years (Yosinski, Clune, Bengio, & Lipson, 2014). The DNN-based AI and analytic techniques have led to a significant evolution in both academic and industrial fields. With the rapid development of modern AI and analytics techniques like convolution neural networks (CNN), generative adversarial networks (GAN), reinforcement learning (RL), and so on, which are based on DNN paradigms, in recent years, there have been a huge number of innovative applications in various domains. For example, long short-term memory (LSTM) techniques have been exploited for predicting stock market prices (Sirignano, & Cont, 2019); CNN techniques have been adopted in surveillance systems, or self-driving cars (Hu & Ni, 2017; Chen, Ma, Wan, Li, & Xia, 2017) and RL methods have created some famous AI applications like Alpha GO (Silver et al., 2016).

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Guest editors:

Haoran Xie

Lingnan University, Hong Kong SAR

Gwo-Jen Hwang

National Taiwan University of Sicence and Technology, Taiwan

Tak-Lam Wong

Douglas College, Canada

Precision education (Yang, 2019) is a new challenge of applying artificial intelligence, machine learning, and learning analytics for improving teaching quality and learning performance. The goal of precision education is to identify at-risk students as early as possible and provide timely intervention based on teaching and learning experiences (Lu et al., 2018). The precision education was inspired by the precision medicine initiative proposed by the former USA President Obama in his 2015 State of the Union address. The emergence of precision medicine is to revolutionize the one-size-fits-all treatment of disease by taking into account individual differences in people’s genes, environments, and lifestyles, as well as by improving the diagnosis, prediction, treatment, and prevention of disease.

Similar to medicine, the current education system is designed not fully considering students’ IQ, learning styles, learning environments, and learning strategies. Inspired by precision medicine, precision education is an innovative approach to emphasize the improvement of diagnosis, prediction, treatment, and prevention of at-risk students, such as diagnosis of students’ engagement, learning patterns and behavior; prediction of students’ learning performance; treatment and prevention with teachers’ timely intervention and well-designed pedagogy, learning strategy, and learning activities. In this special issue, at-risk students are confined to students who were diagnosed could get low academic performance, drop/withdraw a course, or students who were low engaged in terms of learning behaviour, emotion, and cognition.

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Guest Editors

Stephen J.H. Yang

National Central University, Taiwan

Hiroaki Ogata

Kyoto University, Japan

Educational Technology & Society (ET&S) welcomes special issue proposals on specific themes or topics that address the usage of technology for pedagogical purposes, particularly those reflecting current research trends through in-depth research.

For more information, please visit the Special Issue Proposals page.

The ET&S Editorial Office

After the suspension of ET&S journal, the editorial office continues to receive different types of requests, things like people reporting plagiarism issues, people asking for certificates of contributions, publishers/libraries requesting permissions for archiving certain articles or reproducing a specific diagram, potential authors keep sending manuscripts for reviews and various types of queries about the journal matters, etc.

It is not easy to really end a journal which has 22 years of history, at least not from the surface level. This triggers the editorial office to reconsider resuming the operation of ET&S journal and to establish a more sustainable operation model. A stable funding source and enough manpower are the two essential conditions to keep ET&S a fully open access journal. Furthermore, having a well-formed operational guideline is crucial to make the editorial office systematically running and to achieve its sustainability.

The ET&S journal has established a solid and stable editorial office with the support of National Yunlin University of Science and Technology. The new Editors-in-Chief have been appointed aiming to promote innovative educational technology research based on empirical inquires to echo the pedagogical essentials of learning in the real world—lifelong learning, competency-orientation, and multimodal literacy in the 21st century.

The ET&S Editorial Office