Technology integration in higher education has been widely recognised for its multifarious benefits. Nevertheless, arising from various factors, the prevalence of technostress poses a substantial impediment to learning effectiveness. In response, this study employs visualisation analysis and systematic review techniques to formulate a comprehensive model that encompasses variables related to technostress. Based on a systematic selection from 1,861 publications, 83 publications were included to model predictors and outcomes of higher-education students’ technostress. Our findings reveal that the COVID-19 pandemic has spurred growing academic interest in technostress, owing to concerns about the stressful and anxious nature of remote learning. Existing research on this topic predominantly relies on technology acceptance models and theories, with ongoing expansions incorporating variables from multiple research domains. In particular, external factors assume pivotal roles as predictors of technostress, along with subdimensions related to technostress. The impact of technostress can be observed in various aspects, such as learning experiences and performance outcomes. The findings of this study provide valuable insights for future research endeavours, facilitating further exploration and informing technology-enhanced teaching practice.

Artificial intelligence generates vibrant characters, encompassing teachers, peer students, and advisors within diverse educational media. However, the impact of the perceived embodiment of such characters in language learning videos on students’ technology acceptance and adoption is unclear. Integrating structural equation modeling into thematic analysis, this study analyzes 1042 valid responses from higher education students to bridge this research gap. Our study reveals that four subdimensions of embodiment (human-likeness, credibility, learning facilitation, and engagement) significantly and positively predict higher-education students’ perceived ease of use and usefulness of artificial intelligence-generated virtual teachers in language learning videos. Notably, an exception arises, as human-likeness does not significantly predict students’ perceived ease of use in our research context. Students’ perceived systemic interactivity and impact on the learning process emerge as pivotal mediators. The qualitative thematic analysis identifies students’ concerns about classroom administration, developmental support, technical issues, deprived interpersonal collaboration, and liberal attainment cultivation with the virtual teacher presence. This study can illuminate artificial intelligence technology designs and applications in education.

Despite the growing popularity of social network sites (SNSs) in educational settings, there has been a lack of comprehensive review studies focused on the effects of SNSs used for educational purposes. This study seeks to address this gap by examining the impact of SNS use on motivations, attitudes, learning outcomes, and parental involvement, which have been infrequently studied in previous research. Employing a systematic review based on the protocol of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), our findings indicate that the use of SNSs has the potential to enhance learners’ motivations, engender positive attitudes, and improve learning outcomes. However, it is also observed that parental involvement may complicate SNS-assisted learning outcomes. Consequently, this study explores methods for improving SNS-assisted learning motivations, attitudes, and outcomes, as well as the effects of parental involvement on SNS-assisted learning. Future research directions are discussed.

Concerns regarding the potential risks associated with learners’ misusing ChatGPT necessitate an extensive investigation into learner attitudes towards ChatGPT-assisted language learning. This study adopts a mixed-method approach, combining structural equation modeling techniques and interviews. It aims to examine the influencing factors of learner attitudes regarding ChatGPT-assisted language learning under the extended three-tier technology use model from an interdisciplinary perspective, including the technology acceptance model, etc. The study finds that information system quality and hedonic motivation are more significant in contributing to performance expectancy and perceived satisfaction compared to self-regulation in ChatGPT-assisted language learning. Behavioral intention is a better predictor of learning effectiveness in ChatGPT-assisted language learning than perceived satisfaction and performance expectancy. This research also examines the partial or full mediating effects of behavioral intention and performance expectancy between other variables. Although this study is limited by some aspects (e.g., the outdated version of ChatGPT-3 or ChatGPT-3.5), it holds substantial implications for future practice and research. It appeals to more attention from future developers on hedonic motivation and information services of ChatGPT and from future researchers on a more comprehensive insight into influencing factors of learner attitudes towards ChatGPT-assisted language learning.

Synchronous videoconferencing has been chosen as a platform for conducting online courses after the outbreak of the COVID-19 pandemic. However, its acceptance has yet to be explored from interactive and cognitive aspects. This study aims to investigate factors influencing students’ acceptance of the online learning platform- Tencent Meeting/VooV Meeting, extending the UTAUT model by adding community of inquiry and collaborative learning constructs as external factors. A total of 1058 participants’ responses were collected in the autumn semester of 2022, and the survey results were analyzed using structural equation modeling. The results show that most of the influencing factors proposed in the extending UTAUT model apply to Tencent Meeting. Specifically, performance expectancy, social influence, and cognitive presence can positively and significantly predict behavioral intention in using Tencent Meeting for online courses at the 0.001 level. Facilitating conditions and behavioral intention are both determinants of usage behavior. Teaching presence and collaborative learning can positively predict cognitive presence, which is the most influential factor of behavioral intention, indicating that collaborative learning and cognitive presence should be vital considerations in promoting the usage of online learning platforms. The present study expands upon our comprehension of the determinants that impact the utilization of Tencent Meeting, thereby offering recommendations for the development of online platforms according to the cognitive and interactive necessities of students. Instructors may contemplate the creation of online exercises that necessitate higher levels of collaboration to enhance student engagement and encourage cognitive presence, addressing students’ cognitive developmental demands in a supportive manner. This study is limited to the number of selected variables used to explicate the adoption of Tencent Meeting by users with the aid of UTAUT and the community of inquiry framework. It is anticipated that subsequent research may expand upon the array of variables under consideration, such as task-technology fit, usage habits, and information technology competence. The respondents surveyed primarily comprised individuals affiliated with Chinese language universities, with an imbalanced gender distribution, and an over-representation of liberal arts majors. Thus, the generalizability of the research could be impeded. Therefore, to augment the external validity of the study, future research may collect data from various educational levels and cultural backgrounds to corroborate the research outcomes.

The Metaverse, characterized as an interactive and immersive 3D virtual world, is widely recognized for its considerable potential across a range of industries. However, the long-term viability and success of the Metaverse are contingent upon the extent to which users accept and adopt it. Despite this critical aspect, there is a scarcity of research that investigates the factors influencing user acceptance of the Metaverse. To address this research gap, the present study expands upon the technology acceptance model by integrating social and psychological constructs such as social interaction, social presence, conformity, emotional attachment, flow, and perceived enjoyment. The data for this study were obtained through an online survey. A total of 418 responses were collected from Metaverse users, with a response rate of 84%. Partial least squares structural equation modeling was used to analyze the survey data. Results showed that: (1) perceived ease of use (β = 0.214, p < 0,001), emotional attachment (β = 0.375 p < 0,001), and enjoyment (β = 0.194, p < 0,001) could have a positive effect on perceived usefulness; (2) emotional attachment (β = 0.142, p = 0.018), enjoyment (β = 0.179, p = 0.008), and social presence (β = 0.183, p = 0.008) could have a positive effect on perceived ease of use; (3) perceived usefulness (β = 0.432, p < 0,001), perceived ease of use (β = 0.106, p = 0.011), emotional attachment (β = 0.209, p < 0,001), and social interaction (β = 0.209, p < 0,001) have a positive effect on attitudes toward using the Metaverse; (4) perceived usefulness (β = 0.174, p = 0.001), attitudes (β = 0.35, p < 0,001), flow (β = 0.128, p = 0.011), and social interaction (β = 0.106, p = 0.015) could positively influence users’ intention to use the Metaverse. However, social presence could not significantly influence perceived usefulness (β = 0.078, p = 0.141). These results offer important implications for developers and practitioners looking to design and promote the utilization of the Metaverse.

The effectiveness of online meeting platforms is highly associated with users’ acceptance. Nevertheless, few studies have been committed to the roles of social isolation, technostress, and personality in online meeting platform acceptance. This study aimed to investigate the influence of social isolation, technostress, and personality on users’ acceptance of online meeting platforms within the technology acceptance model (TAM) including perceived ease of use, perceived usefulness, attitude towards technology use, and behavioral intention. A total of 975 responses were collected via an online survey. The results revealed that there were positive relationships among four core constructs. But more importantly, social isolation negatively influenced users’ favorable attitudes towards online meeting platforms, and technostress negatively influenced the perception of the usefulness of online meeting platforms. Users with different personalities had different degrees of acceptance of online meeting platforms. The study provides a deep insight into influencing factors in users’ acceptance of online meeting platforms during the rampant COVID-19 pandemic. This study is, therefore, useful for designers and practitioners to optimize the online meeting platforms. In addition, this study adopted TAM to investigate users’ acceptance of online meeting platforms, supporting TAM’s reliability and validity in the online meeting platform-based learning context. Future studies could extend TAM by including specific sociocultural and psychological constructs stemming from the COVID-19 pandemic.