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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.

Educational intelligence can be considered a prized asset in political actors’ careful calculations in setting policy agendas for radical educational transformations in the age of the Fourth Industrial Revolution characterized by Big Data, Artificial Intelligence (AI), machine learning, and the Internet of Things (IoT). As an agent of globalization, the European Union (EU) is uniquely positioned to steer the direction of this new wave of digital technologies for two cardinal objectives in the EU’s rhetorical discourse: social cohesion and economic prosperity. Conversely, its complex governance architecture, which restricts its role in educational policy, tempers its ability to drive policy reforms in education for the strategic and coordinated deployment of Big Data in educational systems to support those twin objectives. This chapter examines this burgeoning policy arena in the European Union by interrogating the most recent policies on the “data economy” enacted at the EU-level and the positionality of education in this newest wave of policy formulation. A content and discourse analysis of policy documents on Big Data reveals that the EU is launching multiple initiatives to regulate these novel technologies across its socio-economic sectors. However, the amorphous nature and unpredictable impact of these technologies, along with the jurisdictional barriers in the education sector stemming from the delimitation of governance layers in the EU, pose difficulties in generating a coordinated approach to policy implementation to engender tangible results. Hence, the contours of an educational intelligent economy in the EU needs considerable policy attention and technical resources in its transition from the current ideational stage to its concrete manifestation.

The implications and impacts of the educational intelligent economy from the vantage point of digital frontierism is explored using a decolonial framework, with a specific focus on Big Data and data sharing in Comparative and International Education (CIE). Recent debates are reviewed about CIE’s past histories and its current directions to tease out their implications for data sharing. The authors demonstrate how data sharing continues to reinforce imperialism through control, dissemination, and application of data, and how electronic and digital colonialism preserve current intellectual and structural hegemonies. Then, we give an example of how donors and funding agencies, including the National Science Foundation, engage in neoliberal scientism and control of data, and how it affects the future of social sciences, including CIE. Our inquiry is at the intersections of economic intelligence and educational intelligence in a rapidly evolving technocentric, data-dominated, and networked economy. The authors demonstrate how educational intelligence in the global economy may exacerbate the asymmetric access to data between the global North and the South, as educational data are increasingly becoming global commodities to be traded between various public and private actors. Finally, the authors argue that decolonial participatory research designs that aim at positive, sustained transformations, as opposed to the stagnancy of Big Data and data mining, should be used to address the problems inherent to the Educational Intelligent Economy.

This study examines the role of emotional intelligence on academic achievement among students at a private university in Kuwait.

The data were obtained through a questionnaire which elicits information on students' sociodemographic data and their overall college grade point average (GPA). The 16-item Wong and Law Emotional Intelligence Scale (WLEIS, Wong and Law, 2002), was used to evaluate the level of emotional intelligence and explore the effect on academic performance in a sample of 480 Kuwaiti college students.

The results of the study indicate that academic success was strongly associated with self-emotion appraisal (SEA) and use of emotions (UOE). However, the results did not show direct correlations with age, high schooling system, gender and nationality. Additionally, results provide supporting evidence that the WLEIS scale has good psychometric properties and can be used as a reliable tool to assess the emotional intelligence skills among college students in Kuwait.

The study has several limitations that require consideration when interpreting the findings. First, this research used a quantitative methodology, which can provide limited information about emotional intelligence, and further qualitative research is necessary to identify contributors and inhibitors of this construct. Second, as in any study using self-report measures, the results may have been influenced by participants' acquiescence and need for social desirability. Further studies should aim to include ways in which EI can be incorporated into academic curricula and qualification framework and barriers that may pertain to encourage emotional intelligence skills development in higher education and suggest solutions accordingly. In future studies it would be interesting to see educators' self-perception vs of students to include a multi-rated for the emotional intelligence. To this end, these areas of study could provide a more comprehensive understanding in the sense of integrating emotional intelligence theories and methods from multiple disciplines that constitute social, personality and psychological trait within higher education. This research has only considered samples from a private university in Kuwait. Extension of sampling scope to other universities around the country and in the Middle East may bring a better understanding of students' emotional intelligence level. In terms of EI components, the results of this study indicated that students score highest in self-emotional appraisal (SEA) and the use of emotions (UOA) and lowest on regulation of emotions (ROE). Additional studies can be conducted to see whether the same results apply on Arab students in the Middle East as a whole. The present study has provided more evidence of the need for cross-cultural comparison of an imported construct and its measurement by showing that the emotional intelligence construct, defined by the WLEIS (Wong and Law, 2002), may be understood differently in other cultures.

There are two key implications in this study, one concerning gender and the other relating to students' GPA. The results suggested differences between the way female and male students viewed EI skills in relation to their academic achievement. Considering that the instrument used to measure EI was the Wong and Law Emotional Intelligence Scale (WLEIS), a self-report measure, perhaps a degree of bias was introduced. Male students' EI scores as a whole (M = 5.56) were higher than the EI mean score for female students (M = 5.39). As Novinger (2001) proposed, emotional expressiveness in the Arab world is such that women are trained to be less demonstrative of their emotions than men.

In addition, gender and cultural values may influence communication styles among Arab students during the teaching process. An awareness of gender and cultural difference related to EI could be beneficial to all parties (students, educators and administrators) in higher educational institutions. Educators' sensitivity to students' EI skills associated with culture can be manifested in a wide variety of teaching practices, ranging from educators' expectations toward students to their interpersonal interactions with students and from teaching styles to assessment methods. For example, an understanding of the possible impact of gender on EI skills may raise educators' levels of cultural sensitivity in dealing with students in the Middle East, particularly, in Kuwait. Even though this study did not show a significant relationship between the overall EI level and students’ GPA, an effect on EI components SEA and UOA was found. University administrators and educators wishing to increase students' academic achievement would do well to incorporate the use and recognition of emotions into their curricula. For instance, emotions can be used to channel the anxiety created by exams to motivate students to prepare more thoroughly and attain more higher standards.

Emotional intelligence skills are important predictors of academic success, and they play a key role in students' performance, and greater the emotional intelligence, the academic achievement will be higher. The results of this study support the research studies suggesting that students' emotional intelligence (EI) should be considered by curriculum designers to enable educators assist their students reach successful academic performance.

The study aims to identify the status quo of artificial intelligence in entrepreneurship education with a view to identifying potential research gaps, especially in the adoption of certain intelligent technologies and pedagogical designs applied in this domain.

A scoping review was conducted using six inclusive and exclusive criteria agreed upon by the author team. The collected studies, which focused on the adoption of AI in entrepreneurship education, were analysed by the team with regards to various aspects including the definition of intelligent technology, research question, educational purpose, research method, sample size, research quality and publication. The results of this analysis were presented in tables and figures.

Educators introduced big data and algorithms of machine learning in entrepreneurship education. Big data analytics use multimodal data to improve the effectiveness of entrepreneurship education and spot entrepreneurial opportunities. Entrepreneurial analytics analysis entrepreneurial projects with low costs and high effectiveness. Machine learning releases educators’ burdens and improves the accuracy of the assessment. However, AI in entrepreneurship education needs more sophisticated pedagogical designs in diagnosis, prediction, intervention, prevention and recommendation, combined with specific entrepreneurial learning content and entrepreneurial procedure, obeying entrepreneurial pedagogy.

This study holds significant implications as it can shift the focus of entrepreneurs and educators towards the educational potential of artificial intelligence, prompting them to consider the ways in which it can be used effectively. By providing valuable insights, the study can stimulate further research and exploration, potentially opening up new avenues for the application of artificial intelligence in entrepreneurship education.

The aim of this paper is to evaluate the kind of evidence and arguments used to support Richard Lynn's increasingly influential doctrine that genetically determined differences in population IQ are the main cause of differences in regional and national levels of socio‐economic development and public health status.

The paper's approach is two‐fold. First, new data on the correlation between regional differences in educational achievement of Italian schoolchildren and regional differences in socio‐economic development are presented in order to test the validity of Lynn's report that there is a progressive North‐to‐South reduction of Italian regional IQ that is highly correlated with a corresponding North‐to‐South reduction in the level of socio‐economic development. Second, a thorough and systematic review of the content of Lynn's article is carried out in order to assess the validity of the data, methods, and arguments normally used to support his socio‐economic doctrine.

Lynn's study uses regional differences in the performance of Italian secondary school children on Organisation for Economic Co‐operation and Development tests of educational achievement to assess regional IQ differences. However, data on Italian regional differences in educational achievement obtained in a much larger INVALSI study of 2,089,829 Italian schoolchildren provide unequivocal evidence that Lynn's educational achievement measure is not a valid index of IQ differences. More generally, the lengthy literature review in Lynn's article reveals uncritical acceptance of reported correlations between any putative index of IQ and socio‐economic variables. Any measure of cognitive performance that is correlated with IQ is considered a measure of IQ, even if there is only a weak correlation. All correlations between such measures and socio‐economic or public health variables are viewed as evidence of direct causal relationships. In all cases, causality is assumed to be in the direction that supports Lynn's doctrine when it would be equally valid to argue that socio‐economic and public health differences cause differences in the performance of IQ tests. In addition to these fundamental logical and statistical errors the present report records numerous other data processing, methodological, and conceptual errors.

The value of the present article is that it demonstrates the flawed manner in which data are interpreted and analysed in order to support Lynn's thesis. Left unchallenged, this pernicious doctrine would promote a socially damaging conception of critically important socio‐economic and public health issues that would discourage the adoption of national policies designed to increase levels of socio‐economic development and improve public health status.

ChatGPT, an artificial intelligence (AI)-powered chatbot, has gained substantial attention in the academic world for its potential to transform the education industry. While ChatGPT offers numerous benefits, concerns have also been raised regarding its impact on the quality of education. This study aims to bridge the gap in research by exploring teachers' perspectives on the adoption of ChatGPT, with a focus on identifying factors that motivate and inhibit them to adopt ChatGPT for educational purposes.

This research has employed a interpretative phenomenological analysis (IPA) qualitative approach. Through in-depth interviews among the teachers, data will be collected to identify the motivating and inhibiting factors that impact teachers' willingness to adopt ChatGPT. The data was collected from 34 teachers working across 10 branches of the University of Technology and Applied Sciences (UTAS) in Oman.

The analysis revealed four themes under motivating factors that encourage teachers to adopt ChatGPT for their educational purpose. These include Theme 1: Exploration of innovative education technologies, Theme 2: Personalization teaching and learning, Theme 3: Time-saving and Theme 4: Professional development. On the other hand, inhibiting factors includes five themes which includes Theme 1: Reliability and accuracy concerns, Theme 2: Reduced human interaction, Theme 3: Privacy and data security, Theme 4: lack of institutional support and Theme 5: Overreliance on ChatGPT.

This study contributes to the understanding of teachers' perspectives on the adoption of ChatGPT in education. By understanding teachers' perspectives, policymakers can design appropriate policies and service providers can customize their offerings to meet teachers' requirements. The study's findings will be valuable for higher education institutions (HEIs) in formulating policies to ensure the appropriate and effective utilization of ChatGPT. The study will provide suggestions to ChatGPT service providers, enabling them to focus on motivating factors and address inhibiting factors, thereby facilitating the seamless adoption of ChatGPT among teachers.

In comparison to previous studies, this study goes beyond merely discussing the possible benefits and limitations of ChatGPT in education. This research significantly contributes to the understanding of ChatGPT adoption among teachers by identifying specific motivating and inhibiting factors that influence teachers to adopt ChatGPT for educational purposes. The research enables to gain important new insights that were not previously found, giving a fresh dimension to the existing literature.

This study aims at the employees’ view on organizational learning (OL). OL is originally assessed in the Strategic Learning Assessment Map (SLAM) at the level of the firm by addressing managers, who rated OL in the SLAM on five dimensions of individual, group, organizational, feed-forward and feedback learning. However, as employees are getting their jobs done discretely and are increasingly making their own decisions, their perspective on OL genuinely matters. Hence, the authors assessed OL at the level of the individual by addressing employees on all levels, who rated OL in a short form of the SLAM (SF-SLAM).

In this paper, the authors focused on the construct validity of this SF-SLAM by investigating its reliability, factorial validity and nomological network. First, they asked whether the SF-SLAM reliably measures OL on five dimensions of individual, group, organizational, feed-forward and feedback learning. Next, they asked whether the SF-SLAM was associated with its nomological network of engaging in innovation-related learning activities, behaving innovatively on the job and showing higher educational levels, intelligence and individual job performances. They used a diverse German employee sample of skilled and unskilled workers and managers (N = 434) and analyzed the data with structural equation modeling.

The SF-SLAM was reliable, but revealed both constrained factorial validity and validity on the basis of its nomological network. First, five dimensions found support in the employee sample, but their correlations were high or very high, except for individual learning. Second, the SF-SLAM showed only few differential relations with variables from its nomological network.

Taken together, the SF-SLAM is short, reliable and only valid for examining individual learning.