Vision, status, and topics of X Reality in Education

The maturity of 5G and artificial intelligence has promoted the XRED (X Reality in Education)'s application and implementation. XRED involves the application of X Reality (i.e., augmented reality, virtual reality, or mixed reality) technologies in the process of instruction and learning. Learning assisted by XR technologies can facilitate students' understanding of spatial structure and function, support their learning of language associations, contribute to long-term memory retention, improve physical task performance, enhance motivation, engagement, and learning outcomes, and promote the development of problem-solving abilities. In this study, we provide a general understanding of XRED by illustrating its development concerning funding support, publication venues, software tools, and research topics with the expectation of promoting its future advance and application. We also highlight the importance and necessity of launching the XRED-focused Elsevier journal: Computers & Education: X Reality

Game-based self-regulated language learning: Theoretical analysis and bibliometrics.

Game-based learning and self-regulated learning have long been valued as effective approaches to language education. However, little research has been conducted to investigate their integration, namely, game-based self-regulated language learning (GBSRLL). This study aims to conceptualise GBSRLL based on the combination of theoretical analysis, thematic evolution analysis, and social network analysis on the research articles in the fields of game-based language learning and self-regulated language learning. The results show that GBSRLL is a new interdisciplinary field emerging since the period from 2018 to 2019. Self-regulated learning strategies that can be performed in GBSRLL, the effects of GBSRLL on learners' affective states, and the features in GBSRLL were the prominent research topics in this field. Its theoretical foundation centres on the positive correlations between learner motivation, self-efficacy, and autonomy and the implementation of game-based learning and self-regulated learning. It is feasible to conduct GBSRLL due to the strong supportiveness of game mechanics for various phases and strategies of self-regulated learning. More contributions to this new interdisciplinary field are called for, especially from the aspects of the long-term effects of GBSRLL on academic performance and the useful tools and technologies for implementing GBSRLL

Topics and trends in artificial intelligence assisted human brain research.

Artificial intelligence (AI) assisted human brain research is a dynamic interdisciplinary field with great interest, rich literature, and huge diversity. The diversity in research topics and technologies keeps increasing along with the tremendous growth in application scope of AI-assisted human brain research. A comprehensive understanding of this field is necessary to assess research efficacy, (re)allocate research resources, and conduct collaborations. This paper combines the structural topic modeling (STM) with the bibliometric analysis to automatically identify prominent research topics from the large-scale, unstructured text of AI-assisted human brain research publications in the past decade. Analyses on topical trends, correlations, and clusters reveal distinct developmental trends of these topics, promising research orientations, and diverse topical distributions in influential countries/regions and research institutes. These findings help better understand scientific and technological AI-assisted human brain research, provide insightful guidance for resource (re)allocation, and promote effective international collaborations

Twenty-five years of computer-assisted language learning: A topic modeling analysis

The advance of educational technologies and digital devices have made computer-assisted language learning (CALL) an active interdisciplinary field with increasing research potential and topic diversity. Questions like “what topics and technologies attract the interest of the CALL community?,” “how have these topics and technologies evolved?,” and “what is the future of CALL?” are key to understanding where the CALL field has been and where it is going. To help answer these questions, the present review combined structural topic modeling, the Mann-Kendall trend test, and hierarchical clustering with bibliometrics to investigate the research status, trends, and prominent issues in CALL from 1,295 articles over the past 25 years ending in 2020. Major findings revealed that Social Sciences Citation Indexed journals such as Computer Assisted Language Learning, Language Learning & Technology, and ReCALL contributed most to the field. Topics that drew the most interest included mobile-assisted language learning, project-based learning, and blended learning. Topics drawing increasing research interest include mobile-assisted language learning, seamless learning, wiki-based learning, and virtual world and virtual reality. Additionally, the development of mobile devices, games, and virtual worlds continuously promote research attention. Finally, the review showed that scholars and educators are integrating different technologies, such as the mixed use of mobile technology and glosses/annotations for vocabulary learning, and their application into various contexts; one such context being the integration of digital multimodal composing into blended project-based learning

Global research on artificial intelligence-enhanced human electroencephalogram analysis

The application of artificial intelligence (AI) technologies in assisting human electroencephalogram (EEG) analysis has become an active scientific field. This study aims to present a comprehensive review of the research field of AI-enhanced human EEG analysis. Using bibliometrics and topic modeling, research articles concerning AI-enhanced human EEG analysis collected from the Web of Science database during the period 2009–2018 were analyzed. After examining 2053 research articles published around the world, it was found that the annual number of articles had significantly grown from 78 to 468, with the USA and China being the most influential and prolific. The results of the keyword analysis showed that “electroencephalogram,” “brain–computer interface,” “classification,” “support vector machine,” “electroencephalography,” and “signal” were the most frequently used. The results of topic modeling and evolution analyses highlighted several important issues, including epileptic seizure detection, brain–machine interface, EEG classification, mental disorders, emotion, and alcoholism and anesthesia. The findings suggest that such visualization and analysis of the research articles could provide a comprehensive overview of the field for communities of practice and inquiry worldwide

Artificial Intelligent Robots for Precision Education: A Topic Modeling-Based Bibliometric Analysis

As a human-friendly system, the artificial intelligence (AI) robot is one of the critical applications in promoting precision education. Alongside the call for humanity-oriented applications in education, AI robot-supported precision education has developed into an active field, with increasing literature available. This study aimed to comprehensively analyze directions taken in the past in this research field to interpret a roadmap for future work. By adopting structural topic modeling, the Mann-Kendall trend test, and keyword analysis, we investigated the research topics and their dynamics in the field based on literature collected from Web of Science and Scopus databases up to 2021. Results showed that AI robots and chatbots had been widely used in different subject areas (e.g., early education, STEM education, medical, nursing, and healthcare education, and language education) for promoting collaborative learning, mobile/game-based learning, distance learning, and affective learning. However, a limited practice in developing true human-centered AI (HCAI)-supported educational robots is available. To advance HCAI in education and its application in educational robots for precision education, we suggested involving humans in AI robot design, thinking of individual learners, testing, and understanding the learner–AI robot interaction, taking an HCAI multidisciplinary approach in robot system development, and providing sufficient technical support for instructors during robot implementation

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

Artificial Intelligence (AI) plays an increasingly important role in language education; however, the trends, research issues, and applications of AI in language learning remain largely under-investigated. Accordingly, the present paper, using bibliometric analysis, investigates these issues via a review of 516 papers published between 2000 and 2019, focusing on how AI was integrated into language education. Findings revealed that the frequency of studies on AI-enhanced language education increased over the period. The USA and Arizona State University were the most active country and institution, respectively. The 10 most popular topics were: (1) automated writing evaluation; (2) intelligent tutoring systems (ITS) for reading and writing; (3) automated error detection; (4) computer-mediated communication; (5) personalized systems for language learning; (6) natural language and vocabulary learning; (7) web resources and web-based systems for language learning; (8) ITS for writing in English for specific purposes; (9) intelligent tutoring and assessment systems for pronunciation and speech training; and (10) affective states and emotions. The results also indicated that AI was frequently used to assist students in learning writing, reading, vocabulary, grammar, speaking, and listening. Natural language processing, automated speech recognition, and learner profiling were commonly applied to develop automated writing evaluation, personalized learning, and intelligent tutoring systems

Discovering thematic change and evolution of utilizing social media for healthcare research

Abstract Background Social media plays a more and more important role in the research of health and healthcare due to the fast development of internet communication and information exchange. This paper conducts a bibliometric analysis to discover the thematic change and evolution of utilizing social media for healthcare research field. Methods With the basis of 4361 publications from both Web of Science and PubMed during the year 2008–2017, the analysis utilizes methods including topic modelling and science mapping analysis. Results Utilizing social media for healthcare research has attracted increasing attention from scientific communities. Journal of Medical Internet Research is the most prolific journal with the USA dominating in the research. Overly, major research themes such as YouTube analysis and Sex event are revealed. Themes in each time period and how they evolve across time span are also detected. Conclusions This systematic mapping of the research themes and research areas helps identify research interests and how they evolve across time, as well as providing insight into future research direction