2023, Volume 26, Issue 1

Special Issue on "Human-centered AI in Education: Augment Human Intelligence with Machine Intelligence"

Guest Editor(s): Stephen J.H. Yang, Hiroaki Ogata and Tatsunori Matsui

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Full Length Articles

Zhi Liu

National Engineering Research Center for Educational Big Data, Faculty of Artificial Intelligence in Education, Central China Normal University, China // zhiliu@mail.ccnu.edu.cn

Ning Zhang

National Engineering Research Center for E-Learning, Faculty of Artificial Intelligence in Education, Central China Normal University, China // zhangning97@mails.ccnu.edu.cn

Xian Peng

National Engineering Research Center for Educational Big Data, Faculty of Artificial Intelligence in Education, Central China Normal University, China // pengxian@ccnu.edu.cn

Sannyuya Liu

National Engineering Research Center for Educational Big Data, Faculty of Artificial Intelligence in Education, Central China Normal University, China // National Engineering Research Center for E-Learning, Faculty of Artificial Intelligence in Education, Central China Normal University, China // lsy.nercel@gmail.com

Zongkai Yang

National Engineering Research Center for Educational Big Data, Faculty of Artificial Intelligence in Education, Central China Normal University, China // National Engineering Research Center for E-Learning, Faculty of Artificial Intelligence in Education, Central China Normal University, China // 13659885363@163.com


ABSTRACT:

Grounded on constructivism, mining a complex mix of social and cognitive interrelations is key to understanding collaborative discussion in online learning. A single examination of one of these factors tends to overlook the impact of the other factor on learning. In this paper, we innovatively constructed a social-cognitive engagement setting to jointly characterize social and cognitive aspects. In the online discussion forum, this study jointly characterized students’ social and cognitive aspects to investigate interactive patterns of different social-cognitive engagements and social-cognitive engagement evolution across four periods (i.e., creation, growth, maturity, and death). Multi-methods including social network analysis, content analysis, epistemic network analysis, and statistical analysis was applied in this study. The results showed that the interactive patterns of social-cognitive engagement were affected by both social network position and cognitive level. In particular, students’ social network position was a vital indicator for the contributions to cognitive level of students, and cognitive level affected the related interactions to some extent. In addition, this study found a nonlinear evolutionary development of students’ social-cognitive engagement. Furthermore, maturity is a critical period on which teachers should focus, as the co-occurrence of social-cognitive engagement reaches a maximum level in this period. Based on the results, this multi-perspective analysis including social and cognitive aspects can provide insightful methodological implications and practical suggestions for teachers in conducting in-depth interactive discussions.


Keywords:

Social-cognitive engagement, Integrated analysis, Social network analysis (SNA), Epistemic network analysis (ENA), Knowledge building

Siu-Cheung Kong

Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong SAR // Centre for Learning, Teaching and Technology, The Education University of Hong Kong, Hong Kong SAR // siucheungkong@gmail.com

William Man-Yin Cheung

Centre for Learning, Teaching and Technology, The Education University of Hong Kong, Hong Kong SAR // williamcheung@eduhk.hk

Guo Zhang

Centre for Learning, Teaching and Technology, The Education University of Hong Kong, Hong Kong SAR // gzhang@friends.eduhk.hk


ABSTRACT:

Emerging research is highlighting the importance of fostering artificial intelligence (AI) literacy among educated citizens of diverse academic backgrounds. However, what to include in such literacy programmes and how to teach literacy is still under-explored. To fill this gap, this study designed and evaluated an AI literacy programme based on a multi-dimensional conceptual framework, which developed participants’ conceptual understanding, literacy, empowerment and ethical awareness. It emphasised conceptual building, highlighted project work in application development and initiated teaching ethics through application development. Thirty-six university students with diverse academic backgrounds joined and completed this programme, which included 7 hours on machine learning, 9 hours on deep learning and 14 hours on application development. Together with the project work, the results of the tests, surveys and reflective writings completed before and after these courses indicate that the programme successfully enhanced participants’ conceptual understanding, literacy, empowerment and ethical awareness. The programme will be extended to include more participants, such as senior secondary school students and the general public. This study initiates a pathway to lower the barrier to entry for AI literacy and addresses a public need. It can guide and inspire future empirical and design research on fostering AI literacy among educated citizens of diverse backgrounds.


Keywords:

Application development, Artificial intelligence literacy, Conceptual framework, Ethical awareness, University students

Yin Yang

Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong // yyin@s.eduhk.hk

Yun Wen

National Institute of Education, Nanyang Technological University, Singapore // yun.wen@nie.edu.sg

Yanjie Song

Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong // ysong@eduhk.hk


ABSTRACT:

The role of self-regulated learning in language learning has been widely acknowledged, and there is a growing number of studies on technology-enhanced self-regulated language learning (SRLL). This systematic review aims to provide a holistic picture of existing studies in this area by identifying the characteristics of published studies, the research methods used to evaluate SRLL effectiveness and the role of technology in SRLL. The review covered 34 empirical studies focusing on SRLL that were published from 2011 to 2020. The results showed varied characteristics of technology-enhanced SRLL studies, dominance of the use of quantitative methods, greater focus on examining students’ SRLL outcomes instead of their processes, and the role of technology in supporting the performance phase of students’ SRLL instead of the entire SRLL process. These findings have implications for using technologies to facilitate and examine the holistic process of students’ SRLL.


Keywords:

Systematic literature review, Technology, Self-regulated language learning (SRLL)

Exploring the Research Trajectory of Digital Game-based Learning: A Citation Network Analysis

Wiwit Ratnasari, Tzu-Chuan Chou and Chen-Hao Huang


Editorial

Human-centered AI in Education: Augment Human Intelligence with Machine Intelligence

Stephen J.H. Yang, Hiroaki Ogata and Tatsunori Matsui

Special Issue Articles

Nabeel Gillani, Rebecca Eynon, Catherine Chiabaut and Kelsey Finkel

Nabeel Gillani

Massachusetts Institute of Technology, USA // ngillani@mit.edu

Rebecca Eynon

University of Oxford, UK // rebecca.eynon@oii.ox.ac.uk

Catherine Chiabaut

The Robertson Foundation, USA // catherine.chiabaut@robertson.org

Kelsey Finkel

The Robertson Foundation, USA // kelsey.finkel@robertson.org


ABSTRACT:

Recent advances in Artificial Intelligence (AI) have sparked renewed interest in its potential to improve education. However, AI is a loose umbrella term that refers to a collection of methods, capabilities, and limitations—many of which are often not explicitly articulated by researchers, education technology companies, or other AI developers. In this paper, we seek to clarify what “AI” is and the potential it holds to both advance and hamper educational opportunities that may improve the human condition. We offer a basic introduction to different methods and philosophies underpinning AI, discuss recent advances, explore applications to education, and highlight key limitations and risks. We conclude with a set of questions that educationalists may ask as they encounter AI in their research and practice. Our hope is to make often jargon-laden terms and concepts accessible, so that all are equipped to understand, interrogate, and ultimately shape the development of human-centered AI in education.


Keywords:

K-12 education, Artificial intelligence in education, Educational data mining, Learning analytics, Natural language processing

Trends, Research Issues and Applications of Artificial Intelligence in Language Education

Xinyi Huang, Di Zou, Gary Cheng, Xieling Chen and Haoran Xie

A Learning Analytics Framework Based on Human-centered Artificial Intelligence for Identifying the Optimal Learning Strategy to Intervene Learning Behavior

Fuzheng Zhao, Gi-Zen Liu, Juan Zhou and Chengjiu Yin

A Human-Centric Automated Essay Scoring and Feedback System for the Development of Ethical Reasoning

Alwyn Vwen Yen Lee, Andres Carlos Luco and Seng Chee Tan


Starting from Volume 17 Issue 4, all published articles of the journal of Educational Technology & Society are available under Creative Commons CC-BY-ND-NC 3.0 license.