Generative artificial intelligence (GenAI) has the potential to transform education, yet little is known about how teachers' perceptions of its autonomy and their risk aversion influence their willingness to adopt. This mixed methods study utilised a human-AI interaction lens to investigate the psychological processes shaping varied adoption. Survey data from 287 teachers across 27 countries and regions were analysed using structural equation modelling with bootstrapped mediation tests, complemented by thematic analysis. Research results revealed that integrating GenAI in teaching is neither straightforward nor effortless. Teachers largely viewed it as semi-autonomous, preferring to retain full control over their teaching. Their awareness of situational factors may contribute to the perception of GenAI as semi-autonomous and weaken the link between artificial autonomy and their adoption intention. As the appeal of automation diminished, concerns about potential harm grew, with risk aversion playing a significant role in adoption decisions. Teachers were more concerned about students' use of GenAI than their own. Overall, GenAI adoption requires careful consideration of context and human-AI interaction. This study enhances understanding of these dynamics, highlighting the psychological processes influencing adoption and offering insights for AI policy and professional training aimed at fostering thoughtful integration into education.
A reflective learning dashboard powered by generative artificial intelligence (AI) was developed to enhance self-regulated learning, student engagement, academic performance and learner perceptions in higher education. Grounded in learning analytics and generative feedback pedagogy, the system integrates behavioural data with AI-generated reflective dialogue to support continuous metacognitive development. Using a quasi-experimental design, 180 undergraduate students participated in a 12-week intervention comparing traditional instructor feedback with AI-generated reflective feedback. Quantitative results revealed significant improvements in self-regulated learning behaviours, time on task, login frequency and academic performance (p < .001), indicating strong effects on both cognitive and behavioural dimensions of learning. In addition, students reported high levels of perceived usefulness, trust, personalisation and positive attitudes towards AI-generated feedback, supported by qualitative insights highlighting increased motivation and learner agency. The findings demonstrate that combining generative AI with learning analytics transforms feedback into a dynamic, dialogic process that enhances reflective thinking and adaptive learning. This study contributes a scalable, human-centred and ethically grounded model for improving both learning outcomes and learner experience in digital education environments. Implications for practice or policy: University administrators should implement transparent AI dashboards ensuring ethics, privacy and accountability. Educators can enhance reflective learning by integrating adaptive AI feedback that supports metacognitive development. Policymakers should promote AI literacy and self-regulated learning within higher education frameworks. Technology developers need to design user-centred and interpretable AI systems aligned with pedagogical objectives. Higher education leaders should foster AI-supported reflection, professional development and evidence-based innovation for sustainable transformation.
Generative artificial intelligence (AI) has rapidly become a dominant topic in educational technology research, raising questions about the visibility of other areas of inquiry that continue to shape the field. In this editorial we examine what the Australasian Journal of Educational Technology (AJET) has published alongside the growth of AI research. We reviewed 143 peer-reviewed research articles published from Volume 39, Issue 1 (2023) to Volume 42, Issue 3 (2026), first identifying articles in which AI was a central focus and then thematically coding the remaining non-AI articles. Fifty articles (35%) were AI-focused, while 93 (65%) addressed other areas of educational technology. These non-AI studies covered a broad range of themes, including professional learning and academic development, assessment and feedback, immersive environments and practice, learning design, learning analytics, collaborative learning, and digital wellbeing. Although non-AI research constitutes nearly two-thirds of the corpus overall, its share of AJET publications has declined as AI-focused research has increased. We argue that making this broader body of work visible matters because it reflects and sustains the conceptual, pedagogical, institutional, and methodological breadth of educational technology research.
Students have faced substantial challenges concerning belonging following the pandemic, particularly related to isolation and loneliness. This study implemented and examined the efficacy of an intervention using personalised emails and learning management system (LMS) based promotional videos designed to enhance membership in student-led clubs and societies. We obtained data from the personalised email platform, LMS and institutional records. Our empirical analysis, using partial least squares modelling, showed a significant and positive association between emails, videos, membership and student grades, thereby supporting the role of personalised emails and LMS videos in promoting club and society membership and improving grades. We also found a significant mediation chain from emails to videos to membership to grades, supporting the value of our integrated approach. However, low socio-economic status had a significant and negative moderating effect on the association between reading emails and watching videos, indicating potential disparities in low socio-economic status students' access to digital resources. Implications for practice or policy: Tertiary institutions should leverage personalised email communications to increase engagement with LMS-based content promoting university clubs and societies membership, associated with improved membership and grade outcomes. Weaker response among low socio-economic status students suggests digital strategies alone are insufficient, and institutions should complement approaches with targeted outreach and alternative communication channels. Tertiary institutions should integrate data from email platforms, LMS systems and institutional records to identify where students disengage and design targeted interventions accordingly.
The rapid integration of generative artificial intelligence (GenAI) in education presents both transformative opportunities and challenges for educators. This study investigated the key determinants influencing students' self-determined motivation to adopt GenAI applications in their learning activities. Using a self-reported questionnaire survey, data from 375 university students in Vietnam were analysed through covariance-based structural equation modelling. Our results indicate that the reliability of GenAI and the personalised learning experience it provides foster students' intention to adopt GenAI, whereas pedagogical drawbacks and concerns over data security and privacy hinder its adoption. Furthermore, our findings reveal that digital competence positively moderates the impact of reliability on students' intention to adopt GenAI. The study highlights the perceived benefits of GenAI in promoting self-determined and personalised learning, alongside its limitations in addressing pedagogical needs and ensuring personal data security thereby enhancingstudent's learning effectiveness within a GenAI-enabled digital learning environment.
