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.

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.