This special issue aims to compile a collection of articles that focus on the application of generative AI in education. Generative AI involves the use of algorithms to generate new and innovative content, which has attracted interest among researchers, developers, and educators. While generative AI has the potential to revolutionize education by providing personalized and adaptive learning experiences for students, there are also concerns regarding the quality and accuracy of the content generated, potential bias in AI algorithms, privacy issues, and the role of AI in classroom and other technology-enhanced learning environments, such as AR/VR, IoT, Robot, simulations, and games.

The goal of this special issue is to explore the ways in which generative AI can be applied in various fields of learning and its potential impact on education and society. Possible topics of interest may include, but are not limited to: 

• The latest advances in generative AI-enhanced learning technology and algorithms.

• The challenges and opportunities of incorporating generative AI in formal or informal learning settings.

• Case studies showcasing and evaluating how generative AI has been deployed in education.

• The pedagogical design for incorporating generative AI into subject learning, 

• Designing AI considering individual differences to generate customized feedback based on personal strengths and weaknesses.

• Explainable AI (XAI): How XAI can be integrated in education to help learners understand how AI models work, why they generate certain results or predictions, and to enable learners to trust and effectively use the generative AI system in a responsible and ethical manner.

• The application of multimodal generative AI to generate content or responses across multiple modes of communication, such as text, images, and sound for creating a more engaging and effective learning experience.

• Advantages and pitfalls in using Generative AI for automated grading and feedback on assignments.

• The ethical implications of using generative AI in content creation.

Mainly, this special issue seeks to advance the understanding of generative AI in education and its potential to transform the learning experience while also highlighting the potential challenges and ethical considerations that must be taken into account.

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Guest Editors:

Jiun-Yu Wu

National Yang Ming Chiao Tung University, Taiwan

Morris Siu-Yung Jong

The Chinese University of Hong Kong, China

Oi-Man Kwok

Texas A&M University, College Station, USA

In an era when digital and immersive technologies surrounding human beings, inclusion, equity, and accessibility become a rising focus to serve and center human in education (Estes et al., 2020; Sulecio de Alvarez & Dickson-Deane, 2018). These can be expressed in ubiquitous technologies, such as captions in media (Downey, 2007) and with mobile devices to access MOOCs (Park et al., 2019). These can also be emerging technologies or special domain research, such as educational design research for participants with special needs or identity using technology in education. With the development of technologies and their evolving affordances, it is becoming crucially important to design and develop these products for life and learning with the needs of all human beings in mind, regardless of race, gender, age, identity expressions, ethnicity identity, socioeconomic status, and abilities. 

This special issue invites papers on educational design research with focus on emerging technologies serving learners with special needs and diverse backgrounds of gender, race, socioeconomic status, ethnicity, identity expression, ability, and capability. Educational design research is defined as “the iterative development of solutions to practical and complex educational problems, and also provides the context for empirical investigation” (McKenney & Reeves, 2019, p. 6). With the iterative nature, educational design research papers in this special issue are anticipated to provide human-centered study and solution with an inclusive lens of novice types of research methods, research process, user or learner experience design, human-product and human-context interactions, product testing and evaluation, and their societal impacts.

This special issue solicits educational design research papers on emerging technologies, with foci on learning access, serving learners with special needs, enhancing their learning progress, and assessing learning with different tools and formats and in different contexts, centering inclusion. The design research can be at different development stages, ranging from needs and context analysis, learner/user experience, prototype design and development, to implementation and evaluation stage.  However, empirical data or evidence at various testing stages (e.g., pilot testing, learner/user experience surveys, formative evaluation, summative evaluation) are strongly encouraged to be included to support the research. The goal of this special issue is to promote the educational research and development of special technologies and create equity and accessibility driven technology-facilitated educational environments.

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Guest Editors:

Xun Ge, Ph.D.

Professor, University of Oklahoma, USA 

Juhong Christie Liu, Ph.D.

Associate Professor, James Madison University, USA

Zhe Li, Ph.D.

Research Faculty, University of Osaka, Japan

The fast development and omnipresence of technology brings great promises for the societal good but also a high level of uncertainty to future life. This uncertainty pushes self-directed learning to the fore since it helps people keep pace with the development and adapt to changes. Self-directed learning, taking responsibility to direct one’s own learning to meet personal goals, is fundamental to living and working in contemporary fast-changing society (Kranzow & Hyland, 2016; Morris, 2019). It is essential to learners’ academic success, engagement and life-long learning (Berger et al., 2021). Self-directed learning depends on both self-direction capacities to enable the overt management of the external learning environment and self-regulation capacities to facilitate the covert management of the cognitive and affective aspects of the internal learning environment (Pilling-Cormick & Garrison, 2007; Rashid & Asghar, 2016). Learners are often found to lack the necessary capacities to engage in self-directed learning (Canty et al., 2019; Gatewood, 2019). Thus, fostering self-directed learning strategies, skills and knowledge is an important educational objective. It is important because it closely relates to essential 21st century skills and lifelong learning skills, which are critical for success in school and workplace. Digital technologies provide rich opportunities for the facilitation of self-directed learning since they make rich information and resources accessible and afford opportunities for independent inquiry and collaborative learning both inside and outside the classroom (Bonk & Lee, 2017; Lai, 2017). Understanding how digital technologies are and can be utilized to facilitate self-directed learning both inside and outside the classroom is an important research agenda. 

Recent studies have shown that technology-rich learning environments can provide conducive learning environment for the development of learners’ self-regulated learning skills (Urbina et al., 2021; Sangsawang, 2020; Manganello et al., 2019). Technology use, social media use in particular, is also found to predict levels of self-directedness in learning (Rashid & Asghar, 2016). However, most of these studies have been conducted in the higher education and adult learning contexts. Self-directed learning among younger learners, the manifestation and influencing factors thereof, might be different from that of adult learners. Morris and Rohs (2021) conducted a scoping review of the potential of digital technology in supporting self-directed learning among children, and the review was only able to locate fourteen papers. Despite confirming the potential of digital technology in supporting children’s self-directed learning process, the authors noted the alarmingly limited research on children’s self-directed learning in technological environments in the formal educational contexts. The authors called for more research in the relationship of self-directed learning and technology among younger learners in both formal and informal learning contexts. 

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Guest Editors:

Dr. Chun Lai 

Faculty of Education, The University of Hong Kong, Hong Kong

Dr. Olga Viberg

Department of Human Centered Technology, Royal Institute of Technology KTH, Sweden

Dr. Chunping Zheng 

Beijing University of Posts and Telecommunications, China

Learner agency is based on the sociocultural theory indicating that language learning takes place through active engagement in the construction of linguistic knowledge (Gao, 2010). It emphasizes the active role of language learners in the language learning process. Learners should take control over their language learning process by making choices and acting on these choices to achieve personal goals (Martin, 2004). Learner agency is especially essential for learning success in online learning environments where diverse multimodal and interactive digital resources such as Ebooks or language learning websites are provided for students to improve their language performance (Knight, & Barbera, 2018; Moore, 2016). Only when students develop learner agency, they can select and tailor the online multimodal resources into personal learning needs and levels. 

Developing learner agency is not an easy task as it involves a series of self-regulated learning tasks for students to conduct in the learning process, including reflection, evaluation, and planning for consecutive learning improvement (Randall, 1999). These self-regulated learning tasks entail the time and efforts from students and the external supports from teachers to teach students to identify learning discrepancy, determine learning goals, arrange learning tasks, and construct clear standards and criteria of evaluation (Bown, 2009; Scanlon, & Connolly, 2021). Challenges facing the students in these tasks consist of their low motivation to hold the responsibility over the learning process and insufficient instructional supports from teachers (Xia, 2014). Most students rely on teachers as capable ones to scaffold their learning process, while teachers found it difficult to provide instructional supports for students with diverse needs.

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Guest Editors:

Dr. Sheng-Shiang Tseng

Tamkang University

Dr. Yun Zhou

Shaanxi Normal University

Although the term microlearning has been around since 2005 (Hug, 2005), its popularity has resurged in recent years as adult learners have become increasingly more mobile and must deal with competing work, family, and educational priorities to invest in lengthy courses or learning materials. Today’s learners can benefit from smaller lessons that deal with a single topic or objective and can be consumed in an accelerated time frame. But what is microlearning exactly? What does a successful microlearning event look like? How long is it? Who is microlearning designed for? And how do you assess the learning in microlearning? These are among the most common questions people ask about the design and implementation of microlearning for today’s learners. Accordingly, the focus of the special issue will be on the design, development, implementation, and assessment of microlearning with an emphasis on designing microlearning with today’s learners in mind.

The benefits of microlearning in training and performance development contexts are well documented as workers need to be able to learn new skills and knowledge quickly to apply them to specific tasks or situations on the job. Microlearning is also starting to take its place in K-12 and higher education. In educational contexts, microlearning strategies can assist in delivering just-in-time informational and instructional content in short, manageable bursts, matching the way learners of all ages are accessing information that interests them in and outside of class.

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Guest Editors:

Dr. Joseph Rene Corbeil 

Professor of Educational Technology, The University of Texas Rio Grande Valley, USA

Dr. Maria Elena Corbeil

Professor of Educational Technology, The University of Texas Rio Grande Valley, USA

The COVID-19 pandemic has prompted unprecedented attention to the social divides in educational access via online and blended learning (e.g., Greenhow et al., 2020, Technology, Pedagogy and Education). The unique affordances and challenges of online learning, along with its psychosocial correlates, have received more attention than ever before. 

Although much opportunistic research has been conducted since the onset of the pandemic, these research efforts have consisted mostly of snapshot studies from a very specific and ungeneralisable moment in time. There is a dearth of research on students’ experiences of online education at the process-level, such as for social interaction. There is even less on teachers’ experiences of delivering online education at the process level (Rasheet et al., 2020, Computers & Education, systematic review).

Accordingly, we seek to convene a special issue of pioneering research that employs longitudinal research methodology to obtain insights into relational dynamics as explanations for divergences in learner experiences and gains from online and blended learning. 

The longitudinal approach may be applied at one or all of the following levels: at the micro-level with measures made from moment to moment; at the meso-level with measures taken over days, weeks or months; or at the macro-level with measures taken over years. Longitudinal data can be collected from either qualitative approaches (e.g., multi-day diary studies), quantitative approaches (e.g., experience sampling survey techniques), or a combination of both. Multiple data, such as self-reported data and the results of empirical treatment, should be collected and analyzed to triangulate the data and increase research validity.

Relational dynamics may be interpersonal, power-related, related to social class, related to cross-cultural differences of communication (e.g., intercultural education), or another construct that authors demonstrate to have significant potential for impact on digital divides. 

Studies focused on any stage of educationーfrom early years to higher educationーare welcome. 

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Guest Editors:

Dr. Nora McIntyre

School of Education, University of Southampton, UK

All interactions are multimodal by nature (Norris, 2004). Humans use various modes to represent meanings and exchange ideas in communication. Multimodal language learning is coined upon the fact that humans integrate multiple modalities including audio, textual, gestural, visual, and spatial resources to learn languages. In second and foreign language learning, skills in interpreting mediated modalities are therefore necessary for language learners, especially when communication takes place in a cross-boundary and cross-cultural context. Digital technologies that allow the combined use of texts, images, audios, videos, and multimedia further intensify these so-called “multimodal possibilities” (Lotherington & Jenson, 2011, p. 227). Although multimodality provides great potential for virtual exchanges in digital platforms, online multimodal interactions can easily overwhelm second language learners (Abrams, 2016; Hampel & Hauck, 2006). With the multimodal affordances of the digital tools, the effects of multimodality on language learners’ meaning-making and language learning in technology-enhanced language learning (TELL) contexts thus deserve further explorations.

As Bax (2003) has foreseen the technology to be progressed towards normalization almost 20 years ago, technology nowadays has become invisible, embedded in everyday practice, and truly integrated into our lives. Language learners are engaged in technology-mediated practices not only in formal schooling but in digital wilds which go beyond contexts within educational settings (Sauro & Zourou, 2019). Language learning in ‘the wilderness’ in online or virtual contexts is rather open, dynamic, multi-faceted, and unpredictable. To have a comprehensive understanding of learners’ multimodal practice and performance in TELL activities, it is essential to contextualize learners’ multimodal language learning and examine the interrelationship between the multimodal potentialities of the online setting, the mediated human activity within that setting, and the characteristics of the learners. The interconnectedness between contexts, TELL activities, and learners thus become more complex and multi-faceted in the digital wilderness. Some questions arise: How does the multimodality feature affect language learning and language use in different online or virtual contexts? How to leverage technologies to foster learners’ multimodal language learning?

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Guest Editors:

Mei-Ling Liaw

National Taichung University of Education, Taiwan

Hsin-I Chen 

National Taipei University of Technology, Taiwan

With the world is becoming more complex and unpredictable, learners should acquire the basic skills to deal with it. The thinking processes associated with and the problem-solving approach of Computational Thinking (CT) allows learners to better deal with the complexity and open-ended non-trivial problems posed by the world and its emerging technologies (e.g., AI, big data, etc.). Therefore, several research studies advocated considering CT as an essential competence that should be included in all educational levels and in every student’s skill set (Grover & Pea, 2018), as part of pathways to provide CT education and literacy. According to the International Computer and Information Literacy Study (ICILS) 2018, CT can be defined as the “ability to identify a problem, break it down into manageable steps, work out the important details or patterns, shape possible solutions and present these solutions in a way that a computer, human or both can understand” (PISA, 2019).

However, despite the increasing attention towards CT in education, it still has several gaps, from different perspectives, that should be addressed to better understand and advance this field. For example, in a recently published review, Tikva and Tambouris (2021) found that CT curricula are poorly conceptualized. In line with this, Lee et al. (2020) reported that several STEM classrooms are failing to integrate CT into their curricula. De Jong and Jeuring (2020), on the other hand, conducted a recent review of CT in Higher Education and revealed that more investigation is needed to identify the set of CT skills and the assessment methods to measure these skills. Additionally, Lyon and Magana concluded that interest in CT is growing, but there is a need for more concrete definitions and implementations (Lyon & J. Magana, 2020). Particularly, there are a number of challenges in ensuring that computing curricula, tools and environments embody appropriate progression and engender motivation for the topic across the years (Howland et al., 2019).

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Guest Editors:

Dr. Ahmed Tlili 

Smart Learning Institute of Beijing Normal University, China

Dr. Daniel Burgos

Universidad Internacional de La Rioja (UNIR), Spain 

Dr. Chee-Kit Looi

National Institute of Education, Nanyang Technological University, Singapore

This special issue will focus on underlying research with the use of AI (Artificial Intelligence) on augmenting human intelligence with machine intelligence in education, where the new design methods and tools can be leveraged and evaluated, hopes to advance AI research, education, policy, and practice to improve the human condition for education. 

The advance of AI in decision-making, prediction, knowledge extraction, and logic reasoning has been making a wider impact on society, economy, and environment (Luan et al., 2020). AI has the potential to educate, train, and augment human productivity, making them better at their tasks and activities. AI can also make a better quality of an individual’s work, resulting in better learning and teaching. With proper use of AI, it can enable human welfare in many ways such as improving the productivity of food, health, water, education, and energy services, but the misuse of AI due to algorithm bias and lack of governance could inhibit human rights and results in jobs, gender, and racial inequality (Vinuesa, et al., 2020).

Human-centered AI can be interpreted from two perspectives, one is AI under human-control (Shneiderman, 2020), and the other is AI concerning human condition (Stanford HAI, 2020). AI under human-control is to leverage the collaboration between human-control and AI-automation to empower human productivity with a high level of reliability, safety, and trust. AI concerning human condition is that AI algorithms taking humanity as the main consideration, require explainable and interpretable computation and judgment process, as well as continuously adjust AI algorithms through human context and societal phenomena to augment human intelligence with machine intelligence, thereby enhancing the welfare of human kinds. 

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Guest Editors:

Stephen J.H. Yang 

National Central University, Taiwan

Hiroaki Ogata

Kyoto University, Japan

Tatsunori Matsui

Waseda University, Japan

Blockchain is one of the ingenious technologies which are disrupting the future of many industries. This encrypted digital ledger technology has all the potential to reshape areas such as healthcare, education, and finance. Education is one such area where these blockchain-based techniques and properties can trigger a wide range of opportunities. In a smart educational environment, the significant challenges faced by its stakeholders are trust, privacy, and transparency-related issues in sharing and retrieval of any information. Since blockchain is a sole technology provides extraordinary features such as decentralization, traceability, and immutability; integrating this technology in a smart educational environment it can overcome all the technical risks, potential threats, and privacy concerns. Whether the educational environment is formal or informal the data can be stored and accessed more securely by using blockchain appropriately. Moreover, the application of blockchain in a smart educational system shall also provide smart assistance for implementation, evaluation, tracking, delivery, and management of any information concerning both the teacher and the learner.

Due to the huge volumes of educational data across various learning platforms, the protection of sensitive and valuable information needs the embracement of robust and intelligent technology. This leads to the development of a decentralized distributed blockchain technology, where each node is secured by a blockchain ledger which can be accessed only by the private key. Furthermore, the principal advantage of the blockchain technology is that the information is stored within the blockchain network with a unique identity, so that when the information is accessed by the users it is checked and validated properly by comparing all the related data. On the other hand, Smart Contracts is a traceable digital transaction facilitator used along with the blockchain which can enhance trust, privacy, and security in virtual or online education. Hence, implementing Blockchain technology in a smart educational environment could make the overall system more secure, reliable and more transparent. 

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Guest editors:

Ching-Hsien Hsu

College of Information and Electrical Engineering, Department of Computer Science, Asia University, Taiwan

Amir H. Alavi

Department of Civil and Environmental Engineering, Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, USA

He Li

Department of Sciences and Informatics, Muroran Institute of Technology, Japan

Data is ingrained in our day-to-day lives. While there are many examples of how it is used for public good, the destructive impacts of unregulated uses of algorithms and big data are felt across sectors including education, law enforcement, healthcare (O’Neil, 2016). It is such that issues of data literacy are also issues of social justice, and education researchers have a role to play in developing data literate citizens (Raffaghelli, 2020).

Data literacy involves skills such as manipulating data sets, selecting and applying appropriate analyses, and making data-based inferences and arguments (Common Core State Standards Initiative, 2010; Franklin et al., 2007; NGSS Lead States, 2013); but it also involves a critical understanding of what and how data are produced and tracked, of how they can be used for particular purposes, and of the role of context in interpretations of data. Such a critical lens is essential for recognizing how data reflect the biases inherent in the systems that create and use them (Kitchin, 2014) how data are subject to multiple (mis)interpretations (Pangrazio & Selwyn 2018), and how data can echo power relations in society (Van Wart, Lanouette & Parikh, 2020). Without such abilities, there is a risk of reproducing societal inequities; and of further entrenching those in power, and those most vulnerable (O’Neill, 2016; Philip et al., 2013).

This special issue will build on recent research on the role of data literacy education for promoting social justice (Raffaghelli, 2020). In particular, we seek research that examines the transformative potential in the intersection of data literacy, education, and social justice, including racial justice, economic justice, environmental justice, and spatial justice. Together, we expect the contributions to this issue to examine such questions as:

1. What dimensions of learning (e.g., technical, socio-political) are most important in a complete account of critical data literacy? How can we best support learners’ growth in these dimensions?

2. How can data literacy education empower people, individually and collectively, to be agentic in both their local and global communities?

3. How can learning experiences be created that make visible each of the personal, community, and societal interests embedded in data?

More broadly, we anticipate that this special issue will offer examples of designs and frameworks that other researchers may use to align their work—whether or not their primary focus is explicitly on social justice—with social justice values. 

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Guest Editors:

Camillia Matuk

New York University

Simon Knight

University of Technology, Sydney

Kayla DesPortes

New York University

At the doorstep of the third decade in the 21st century, fast-growing computing technologies boost the adoption of diverse devices and applications in the educational area, which dazzle instructors and learners. The outbreak of COVID-19 since the first month of 2020 made all learning activities online in many countries and territories, which adopted social distancing approaches to contain spread of virus. Unfortunately, many teachers and students have felt overwhelmed by such a drastic change in learning behaviour, though digital learning had been around for decades, including big efforts put in MOOC movements in different sectors. Quick adoption of digital means of learning and teaching which is the abnormality of teaching and learning could quickly fade out after the lifestyle get back to normal. It is interesting to know if the unexpected pandemic brings the yet-to-come education evolution earlier.

Although being competitive, the post-pandemic recovery is on the agenda. How the educational sector can stand with the contingency and bounce back stronger with insights gained during the pandemic pose interest. It pictures how the post-pandemic human development and learning looks like, allowing it to potentially shift from just content dissemination to augmenting relationships with teachers, personalization, and independence.

Evaluating the effectiveness and knowing in which environments the advanced technologies work better, and improving learning activities from both the students’ and instructors’ perspective are critical for the next generation delivery of the learning content. Given their comparative novelty, to what extent instructors and learners can accept and get accommodated to them sustain the ongoing update and development of new technologies. There are huge challenges ahead for understanding and bridging the gap in implementation of multi-mode digital learning over the coming decade.

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Guest Editors:

 Jun Shen 

University of Wollongong, Australia

Samuel Fosso Wamba

Toulouse Business School, France

Alex Shvonski

MIT, USA

Tingru Cui

University of Melbourne, Australia

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

Creativity is critical component of any learning programs because it is considered as the most important 21st century skills (Bryant, 2010; Lin, Shadiev, Hwang, & Shen, 2020; Rhodes, 1987; Shadiev, Huang, Hwang, & Liu, 2017; Sternberg & Lubart, 1999). Creativity is the ability to produce work that is original and useful; produced work can be intangible such as an idea or tangible such as an essay (Sternberg & Lubart, 1999). Creativity relates not only to the product that results from creative activity but also to the person who creates it, the cognitive processes involved in the creation of work, and the environmental influences (Mayer, 1989; Rhodes, 1987). Creative learning helps learners be innovative, learn new things, try out new ideas, and new ways of thinking and problem-solving. Scholars concluded that creativity is very important in today’s world of innovations and therefore, creative performance needs to be facilitated.

Authentic learning environments play crucial role in promoting creative skills development in learners (Davies et al., 2013; Jindal-Snape et al., 2013). An authentic environment here is defined as an environment that “preserves the complexity of the real-life context with rich situational affordances” (Herrington & Oliver, 2000, p. 180). Authentic learning environments contains a wide range of available resources that may stimulate learner creativity and make use of such resources supports the growth of ideas. Furthermore, authentic learning environments give learners greater freedom for imagination, provide rich contexts for the purpose of discovering learner schemas and interests. Scholars also argued that authentic contexts reflect the way that the knowledge will be used by learners in their real life (Herrington & Oliver, 2000; Shadiev, Hwang, & Huang, 2017). Therefore, creative learning in authentic learning environments need to be encouraged.

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Guest editors:

Rustam Shadiev 

Nanjing Normal University, China

Wu-Yuin Hwang

National Central University, Taiwan

Gheorghita Ghinea

Brunel University London, United Kingdom

The concepts of embodiment and embodied learning are gaining traction in the field of education, these concepts are deeply rooted in theories of Embodied Cognition (Barsalou, 2008; Wilson, 2002).  New educational technologies enable researchers and practitioners to include more gestures and body movements into learning designs, creating immersive and gesture-rich learning environments (Georgiou & Ioannou, 2019; Dede, 2009; Johnson-Glenberg, 2018; Lindgren & Johnson-Glenberg, 2013; Lindgren, Tscholl, Wang, & Johnson, 2016; Minocha, Tudor & Tilling, 2017). Such embodied environments should enable multi-modal and multi-sensory forms of interaction through gestures and bodily movement, tactile and auditory sensory experiences. While the interplay of new forms of technology and learning is complex, recent evidence suggests that learning experience design, pedagogy, and practice with embodied learning technologies can have an important effect on learning, engagement, and achievement in all educational settings -- formal, non-formal and informal. This special issue aims to synthesize current knowledge on the design and evaluation of learning in immersive and embodied environments. The aim is to provide insights on best practices for learning design based on systematic or empirical data and analysis on learning outcomes or processes.

The specific scope is to publish research that addresses learning in immersive and embodied environments. The focus is not the technology per se, but rather issues related to learning design, the process continuum of learning, teaching, and assessment and how they are affected or enhanced using technologies, including gaming environments, escape rooms, VR and AR environments etc.  We are not seeking theory papers or meta-analyses, but rather, evidence-based and impactful educational applications and research, meshing pedagogy and practice in these environments. The technology under consideration is augmented, virtual, or mixed reality (now called XR), and will include relevant work on haptics i.e., gloves, hacked controllers, or other tactile simulators, if they are used to further learning.

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Guest editors:

Andri Ioannou

Cyprus University of Technology, Research Center on Interactive Media, Smart Systems and Emerging Technologies

Kaushal Kumar Bhagat

Centre for Educational Technology, Indian Institute of Technology, Kharagpur

Mina Johnson-Glenberg

Arizona State University, Embodied Games

The term STEM (science, mathematics, technology and engineering) has become a buzzword among the global education practitioners who have called for curriculum reforms that will boost the competitiveness of the next generation by nurturing their problem-solving ability and creativity (Jane, Jong, & Chai, 2019). STEM education refers to “solving problems that draw on concepts and procedures from mathematics and science while incorporating the teamwork and design methodology of engineering and using appropriate technology” (Shaughnessy, 2013, p. 324). Simply put, it serves as a means to integrate different disciplines as used in tackling real-life problems. In the long term, this cross-disciplinary subject is expected to enhance students’ problem-solving, critical and analytical thinking skills, and cultivate them to be constructive and innovative citizens (Jong, 2015; Merrill, 2009).

The significance of STEM education in today’s technologically-dominated world cannot be underestimated. STEM competencies, nowadays, are not only required within but also outside of the STEM occupations (So, Jong, & Liu, 2020). In this regard, the development of students’ STEM competencies has become an urgent goal of many education systems around the globe, especially in K-12. The U.S. government has heavily invested in STEM education by implementing some state-level initiatives. For example, The “Educate to Innovate” initiative, launched in 2009, aims to enhance STEM literacy, improve teaching quality and increase educational and career opportunities for the youth through the collaboration between the government, the private sector and the non-profit and research communities (Burke & McNeill, 2011). In the U.K., the STEM education reform aims to ensure the provision of qualified people in the STEM workforce and the development of STEM literacy for the public (Department of Education and Skills, 2006). In Asian countries such as Korea, Hong Kong, Taiwan, China and Japan, STEM education has also emerged as an important curriculum reform (Ritz & Fan, 2015; So et al., 2020). 

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Guest editors:

Morris S. Y. JONG

The Chinese University of Hong Kong, HKSAR

Yanjie SONG

The Education University of Hong Kong, HKSAR

Elliot SOLOWAY

University of Michigan, USA

Cathleen NORRIS

University of North Texas, USA

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

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