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The application of artificial intelligence chatbots is an emerging trend in educational technology studies for its multi-faceted advantages. However, the existing studies rarely take a perspective of educational technology application to evaluate the application of chatbots to educational contexts. This study aims to bridge the research gap by taking an educational perspective to review the existing literature on artificial intelligence chatbots.

This study combines bibliometric analysis and citation network analysis: a bibliometric analysis through visualization of keyword, authors, organizations and countries and a citation network analysis based on literature clustering.

Educational applications of chatbots are still rising in post-COVID-19 learning environments. Popular research issues on this topic include technological advancements, students’ perception of chatbots and effectiveness of chatbots in different educational contexts. Originating from similar technological and theoretical foundations, chatbots are primarily applied to language education, educational services (such as information counseling and automated grading), health-care education and medical training. Diversifying application contexts demonstrate specific purposes for using chatbots in education but are confronted with some common challenges. Multi-faceted factors can influence the effectiveness and acceptance of chatbots in education. This study provides an extended framework to facilitate extending artificial intelligence chatbot applications in education.

The authors have to acknowledge that this study is subjected to some limitations. First, the literature search was based on the core collection on Web of Science, which did not include some existing studies. Second, this bibliometric analysis only included studies published in English. Third, due to the limitation in technological expertise, the authors could not comprehensively interpret the implications of some studies reporting technological advancements. However, this study intended to establish its research significance by summarizing and evaluating the effectiveness of artificial intelligence chatbots from an educational perspective.

This study identifies the publication trends of artificial intelligence chatbots in educational contexts. It bridges the research gap caused by previous neglection of treating educational contexts as an interconnected whole which can demonstrate its characteristics. It identifies the major application contexts of artificial intelligence chatbots in education and encouraged further extending of applications. It also proposes an extended framework to consider that covers three critical components of technological integration in education when future researchers and instructors apply artificial intelligence chatbots to new educational contexts.

A majority of higher education institutions (HEIs) in Oman, and internationally, have adopted English as the language of education, driven by its power and its globally accepted status as the language of knowledge and communication. Such an internationalisation policy has been inadequately evaluated to examine its actual effects. This paper aims at analysing the existing literature with a view to hypothesise the effects of adopting English as a medium of instruction (EMI) on establishing intellectual capital in the Omani context.

The paper employs a case study design that draws on data generated through a systematic review of 94 peer-reviewed papers that are synthesised using thematic analysis.

The findings indicate that EMI negatively affects the optimal creation of intellectual capital through limiting access to HE, hindering knowledge transfer, impeding Omanis' employability and hindering faculty's professional growth. EMI leads HEIs to mirror the supplying countries' cultures in terms of materials, ideologies and standards. It affects teaching and research quality, training and communication, the sense of equity, belonging and self-worth amongst students and the relationships amongst faculty members. It also increases reliance on external stakeholders.

The paper highlights the interconnection between the forms of intellectual capital and how some components are antecedents to the creation of the intellectual capital forms. It establishes the moderating role the language of instruction plays in relation to the three sub forms of intellectual capital in higher education.

The paper calls for maximising higher education intellectual capital through adopting bilingual rather than monolingual higher education. It calls upon policymakers to revisit the assumptions underlying higher education systems in order to optimise their outcomes.

The paper is the first one that sheds light on the role of language in intellectual capital construction. Such a moderating role has received almost no attention in the higher education literature that is largely busy quantifying its outcomes rather than ensuring they are actually sustainably generated.

This study aims to discover how domain familiarity and language affect the cognitive load and the strategies applied for the evaluation of search engine results pages (SERP).

This study used an experimental research design. The pattern of the experiment was based upon repeated measures design. Each student was given four SERPs varying in two dimensions: language and content. The criteria of students to decide on the three best links within the SERP, the reasoning behind their selection, and their perceived cognitive load of the given task were the repeated measures collected from each participant.

The evaluation criteria changed according to the language and task type. The cognitive load was reported higher when the content was presented in English or when the content was academic. Regarding the search strategies, a majority of students trusted familiar sources or relied on keywords they found in the short description of the links. A qualitative analysis showed that students can be grouped into different types according to the reasons they stated for their choices. Source seeker, keyword seeker and specific information seeker were the most common types observed.

This study has an international scope with regard to data collection. Moreover, the tasks and findings contribute to the literature on information literacy.

In an ever-interconnected world dominated by discourses on the internationalization and marketization of higher education, concerns related to language and employability have been the focus of recent debates. There is, however, a dearth of research investigating how these dimensions relate to one another in recent comparative and international higher education research. By focusing on how issues related to language and employability have been presented in recent higher education research worldwide, this chapter aims to contribute to our understanding of this concern. To achieve this goal, we conducted a scoping literature review using the Web of Science, Scopus, and the Education Resources Information Center (ERIC) databases, considering the years 2011–2020. The findings, perhaps not surprisingly, suggested that language skills are perceived to be valued by both graduates and employers though the discussions predominantly focused on one language, English. The research focus on English for employability in Anglophone contexts is understandable. However, the fact that the trend is observed in contexts where the language is not the primary or official language seems to indicate the influence of internationalization of higher education and global labor markets primarily dominated by English. The literature also suggested that (English) language training in higher education programs needs to move from solely linguistic and qualification-related content areas to a broader sphere of English for communication purposes that cover both specialized disciplinary content and broader generic employability skills. Considering this finding, we suggest that higher education systems and institutions incorporate recent developments in English for occupational purposes in their curriculum. We also recommend that there needs to be a shift from the overwhelmingly English language-dominated discussions to more inclusive research that assesses the impact of other dominant languages on employability-related concerns.

An emerging research stream focuses on the place-based ecosystems where artificial intelligence (AI) innovations emerge and develop. This literature builds on the contextual turn in management research and, specifically, work on entrepreneurial ecosystems. However, as a nascent research area, the literature on AI and entrepreneurial ecosystems is fragmented across academic and practitioner boundaries and unconnected disciplines because of disparate and ill-defined concepts. As a result, the literature is disorganized and its main insights are latent. The purpose of this paper is to synthesize research on AI ecosystems and identify the main insights.

We first consolidate research on the “where” of AI innovation through a scoping review. To address the fragmentation in the literature and understand how entrepreneurial ecosystems are associated with AI innovation, we then use content analysis to explore the literature.

We identify the main characteristics of the AI and ecosystems literature and the key dimensions of “AI entrepreneurial ecosystems”: the local actors and factors in geographic territories that are coordinated to support the creation and development of AI technologies. We clarify the relationships among AI technologies and ecosystem dimensions and uncover the latent themes and underlying structure of research on AI entrepreneurial ecosystems.

We increase conceptual precision by introducing and defining an umbrella concept—AI entrepreneurial ecosystem—and propose a research agenda to spur further insights. Our analysis contributes to research at the intersection of management, information systems, and entrepreneurship and creates actionable insights for practitioners influenced by the geographic agglomeration of AI innovation.

The purpose of this paper is to explore issues pertaining to monolingual ideology in the United States and the challenges in terms of career identity and development for multilingual individuals.

This conceptual paper provides a discussion of the relevant literature pertaining to linguistic diversity, language ideologies, career identity and career development, and offers a critical conceptual framework for understanding career development in linguistically diverse, multilingual contexts.

Based on a critical review of literature, this paper proposes a conceptual framework which can be used to address linguistic issues that may otherwise encourage discrimination and inequity in the workplace.

This paper addresses the gap in career development literature by proposing a critical conceptual framework that integrates language as an important element of one’s career identity.

The research on structure function recognition mainly concentrates on identifying a specific part of academic literature and its applicability in the multidiscipline perspective. A specific part of academic literature, such as sentences, paragraphs and chapter contents are also called a level of academic literature in this paper. There are a few comparative research works on the relationship between models, disciplines and levels in the process of structure function recognition. In view of this, comparative research on structure function recognition based on deep learning has been conducted in this paper.

An experimental corpus, including the academic literature of traditional Chinese medicine, library and information science, computer science, environmental science and phytology, was constructed. Meanwhile, deep learning models such as convolutional neural networks (CNN), long and short-term memory (LSTM) and bidirectional encoder representation from transformers (BERT) were used. The comparative experiments of structure function recognition were conducted with the help of the deep learning models from the multilevel perspective.

The experimental results showed that (1) the BERT model performed best, with F1 values of 78.02, 89.41 and 94.88%, respectively at the level of sentence, paragraph and chapter content. (2) The deep learning models performed better on the academic literature of traditional Chinese medicine than on other disciplines in most cases, e.g. F1 values of CNN, LSTM and BERT, respectively arrived at 71.14, 69.96 and 78.02% at the level of sentence. (3) The deep learning models performed better at the level of chapter content than other levels, the maximum F1 values of CNN, LSTM and BERT at 91.92, 74.90 and 94.88%, respectively. Furthermore, the confusion matrix of recognition results on the academic literature was introduced to find out the reason for misrecognition.

This paper may inspire other research on structure function recognition, and provide a valuable reference for the analysis of influencing factors.

Testing business processes is crucial to assess the compliance of business process models with requirements. Automating this task optimizes testing efforts and reduces human error while also providing improvement insights for the business process modeling activity. The primary purposes of this paper are to conduct a literature review of Business Process Model and Notation (BPMN) testing and formal verification and to propose the Business Process Evaluation and Research Framework for Enhancement and Continuous Testing (bPERFECT) framework, which aims to guide business process testing (BPT) research and implementation. Secondary objectives include (1) eliciting the existing types of testing, (2) evaluating their impact on efficiency and (3) assessing the formal verification techniques that complement testing.

The methodology used is based on Kitchenham's (2004) original procedures for conducting systematic literature reviews.

Results of this study indicate that three distinct business process model testing types can be found in the literature: black/gray-box, regression and integration. Testing and verification approaches differ in aspects such as awareness of test data, coverage criteria and auxiliary representations used. However, most solutions pose notable hindrances, such as BPMN element limitations, that lead to limited practicality.

The databases selected in the review protocol may have excluded relevant studies on this topic. More databases and gray literature could also be considered for inclusion in this review.

Three main originality aspects are identified in this study as follows: (1) the classification of process model testing types, (2) the future trends foreseen for BPMN model testing and verification and (3) the bPERFECT framework for testing business processes.