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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.

The purpose of this paper is to analyze agricultural total factor productivity (TFP) and input redundancies in different regions of China, and to bring out the policy implications for improving efficiency in agricultural production as well as environment protection.

Based on the provincial panel data during 1995-2014, the agricultural productivity of China and its regional disparity are analyzed. First, the agricultural TFP and its decomposition are dynamically evaluated by means of data envelopment analysis-Malmquist productivity index. Second, the agricultural radial production efficiency in year 2014 and the input redundancy changes from 1995 to 2014 are measured based on the BCC-slacks-based measure model.

The results showed that the overall agricultural TFP of China grew 4.3 percent annually during 1995-2014, mainly as a result of technical progress. However, the declines of technical efficiency and scale efficiency slowed down the agricultural TFP growth. The TFP growth in the Western region and Central region far exceeded the Eastern region in last few years. In 2014, most effective decision-making units were in the Western region. The input redundancies in the agricultural production increased substantially after 2006, especially for the pesticide use amount, reservoir capacity and agricultural machinery power.

Combining the dynamic and static analyses, the paper fulfilled the study of China’s agricultural productivity and the input redundancies in recent years, and also presented the regional disparities.

The present study examined the psychometric properties of the EGame- flow scale in a Mexican sample, presenting evidence of construct validity (exploratory factor analysis and confirmatory factor analysis), reliability (Cronbach’s alpha and McDonald’s omega) and discriminant validity (mean variance extracted).

Participants: Of the 255 Mexican participants in the non-probabilistic sample who had previously interacted with the LOST logistics simulator, 166 (65%) were men and 89 (35%) were women; their ages ranged from 22 to 45. The statistical packages SPSS 25, JASP 0.16 and AMOS 23 facilitated the corresponding analyses. First, we calculated the means and standard deviations of the scale items. Next, we performed an exploratory factor analysis to examine the measurement model’s internal structure and a confirmatory factor analysis to confirm the structure proposed in the exploratory factor analysis. To analyze the internal structure of the measurement model so that the estimates were not affected by multivariate normality problems, we utilized the AMOS bootstrap method (with 500 repetitions, 95% CI), the maximum likelihood (MV) estimation method, and the fit indices: X2, p (chi-square and associated likelihood), Tucker–Lewis index (TLI), standardized statistical mean square residual (SRMR), comparative fit index (CFI) and root mean Square error approximation (RMSEA) with its confidence interval, the values of X2 with p < 0.001; TLI, CFI, AGFI = 0.95; RMSEA and SRMR = 0.08 (Byrne, 2016). Finally, we estimated the reliability of the measurement model with Cronbach’s alpha (a), McDonald’s omega (ω) coefficient and the mean variance extracted (VME).

An exploratory factor analysis with the MV method and obliminal rotation showed a good fit of the data to the model, which aligns with the significance of the Barlette sphericity test (X2 = 8443.2, p < 0.000) and the Kaiser–Meyer-Olkin (KMO) value of 0.94. The indices confirmed the fit of the data to the six-dimensional model for measuring the users' level of enjoyment of online games (X2 = 678.2 gl = 411, p = 0.000; SRMR = 0.05; TLI = 0.95, CFI = 0.95 and RMSEA = 0.05, IC 90% [0.04, 0.05]).

The self-reporting format of the scale increases the social desirability of the responses, but the sample only collects information from a specific geographic location, so these findings cannot extrapolate to populations with very marked cultural differences. Finally, the study did not measure other validity evidence, such as predictive and concurrent validity, which should be considered in future studies.

From a practical perspective, the study offers a measurement scale with fewer items and robust psychometric evidence that ensures the fit of the data to the EGame-flow measurement scale. Further research must continue to learn about the behavior of the EGame-flow scale in different samples that new evidence of psychometric properties continues to appear and that other factors associated with the users' gaming enjoyment experience are analyzed.

The value and originality of the study lie in the type of evidence of psychometric properties that the instrument has and particularly in the style of sample in which the study is carried out, in this case, in the context of Mexico, where there are not enough instruments that measure the flow experience of users.

This paper aims to answer two research questions: first, to study the factors that directly and indirectly influence the intentions of job-seekers and second, to examine the moderating role of gender differences in e-recruitment adoption through the application of technology acceptance model (TAM) in India.

A convenience sampling technique was used to collect online data via GoogleDocs through various online channels such as social media, LinkedIn and email. The final data was collected from 364 final-year graduates and postgraduate students to confirm the impact of female and male differences, measurement invariance in composite models (MICOM) and multi-group analysis (MGA).

The results indicated that perceived usefulness (PU) and perceived ease of use (PEOU) have a direct impact on attitude (AT), whereas PU influenced behavioral intentions (BI) of job-seekers, but PEOU did not. AT directly leads to the BI. The outcomes of mediation analysis show that AT partially mediates the relationships between PU to BI and PEOU to BI. Further, the findings of MICOM and MGA showed that gender significantly moderates all the relationships between the constructs except for the influence of AT on BI.

This study contributes to the current literature, revealing that the original TAM model is still pertinent and effective in prevailing periods in emerging markets. The significance of PEOU and PU on AT and BI implies that job-seekers will strongly adopt e-recruitment when it is user-friendly and assist them to accomplish their tasks easily and efficiently. Moreover, gender has a vital moderating influence in e-recruitment adoption. In the case of females, the effect of PEOU is stronger, and for males, PU has a substantial impact on adoption.

Developers and recruiters should provide significant information related to salary, location and job profile on e-recruitment to enhance the adoption rate of online recruitment. Further, the usefulness of e-recruitment systems was more significant for males compared to females, whereas female job-seekers prefer the e-recruitment system, which is easy to use and operate.

This research fills a gap in the literature by examining the essential factors affecting the BI of job-seekers as well as empirically testing the impact of gender differences to adopt TAM for e-recruitment – an under-explored subject in developing countries like India.

This study aims to contribute to literature on mobile learning (m-learning) by proposing four research clusters whereby scholars can expand m-learning research to facilitate effective learning experiences for students.

This study reviews student-centric literature on m-learning since 2010 and presents insights on m-learning while applying well-established bibliometric techniques. Consequently, 722 articles published in the past decade were evaluated by identifying key research areas, most influential authors, countries, journals and organisations. Most influential studies based on number of citations were also examined.

Through article co-citation analysis, four clusters representing m-learning literature were identified: concept of m-learning, application of m-learning in education, designing framework for model learning/acceptance and emerging technologies.

As mobile learning (m-learning) has undergone an evolution from being an emerging field to a significant teaching and research tool, it is pertinent to explore and identify the trends of m-learning research.