Amid increasing attention to learner engagement in technology-supported English language teaching settings, this paper presents how action research can help a university teacher refine strategies to enhance student engagement. Based on a collaborative pedagogical model, the research introduced Indexing and a structured group activity that allowed students to explore key concepts collaboratively, reflect on their progress and record their roles using digital platforms. Through repeated cycles of planning, action, observation and reflection, the teacher identified the primary barriers to engagement and adjusted instructional strategies accordingly. Data collected from classroom observations, interviews, reflective journals and surveys demonstrated that combining action research with digital tools created a more student-centred environment, indicated by greater learner agency, collaboration and motivation. The intervention transformed the classroom into a dynamic, shared learning space where students actively participated in meaningful technology-supported learning experiences.
Anxiety among university students has become a critical global public health issue due to its high prevalence and impact. This study builds on prior work on virtual therapeutic landscape design and evaluation by operationalising therapeutic landscape principles in a Virtual reality setting, aiming to develop and evaluate a multi-sensory immersive intervention for anxiety relief. Drawing on semi-structured interviews and the analytic Werarchy process, four core components-sensory, interactive, personalised and content experience - were identified, forming a systematic evaluation framework. A within-subject pre-post study involving 46 university students demonstrated that short-term virtual therapeutic landscape exposure significantly reduced state anxiety (p < 0.001) d = 0.916% Hegative affect ( p < 0.001 , d = 0.588 ) and enhanced subjective vitality ( p < 0.001 , d = 0.966 ). Physiological data showed decreased phasic skin conductance response and phase-related changes in tonic skin conductance level across baseline-intervention-recovery, both showing significant time effects. By contrast, virtual therapeutic landscape exposure produced comparatively smaller changes in trait anxiety ( p < 0.001 , d = 0.739 ) and positive ffect i_{p} = 0.005 d = 0.437 ) Taken together, virtual therapeutic landscape yielded convergent psychological and physiological benefits and provides an empirically grounded extension of therapeutic landscape theory into digital mental health interventions. Implications for practice or policy: Universities could use virtual therapeutic landscape as a low-burden adjunct to support short-term anxiety relief in students. Student support services can position virtual therapeutic landscape as a brief intervention for reducing state anxiety and negative affect rather than as a substitute for longer-term treatment. Designers of virtual reality mental health interventions should prioritise sensory Immersion, user-friendliness and system stability to improve restorative outcomes.
This systematic literature review of 39 peer-reviewed empirical articles outlines a usage framework comprising seven ways higher education students have utilized Generative Artificial Intelligence in their learning tasks since January 2018 and examines the resulting actual and perceived learning outcomes. Findings indicate that students’ actual learning outcomes achieved through Generative Artificial Intelligence were predominantly successful, while perceived outcomes vary, presenting a mixed picture of success and challenges. Specifically, when students used Generative Artificial Intelligence as a translator, refiner, navigator, evaluator, dialoguer, or self-regulatory supporter, they perceived higher success-to-challenge ratios in learning effectiveness, learning efficiency, interactivity, self-regulation, and personalized learning. By contrast, using Generative Artificial Intelligence as a creator resulted in an approximately equal proportion of successes and challenges. Notably, Generative Artificial Intelligence as a self-regulatory supporter resulted in the lowest success-to-challenge ratio among all usage types, with the few challenges attributed to insufficient holistic integrated self-regulated learning skills. The findings suggest that students need to enhance their comprehensive self-regulated learning capabilities to optimize Generative Artificial Intelligence use in learning task.
This editorial introduces Volume 42, Issue 2 of the Australasian Journal of Educational Technology and develops a theme that draws its eight papers together: as educational technologies become more capable, the human work surrounding their use becomes more visible and more consequential. Across studies of mathematics learning, collaborative learning design, medical education, programming education, language research, English language teaching, mathematical creativity, and immersive interventions for anxiety, we trace this human work in three forms: relational, metacognitive, and ethical. We argue that the value of a technology depends less on what the technology does than on the pedagogical, relational, and self-regulatory work that surrounds it, and that this work must be deliberately designed rather than assumed. A second thread runs beneath the first: several contributions are concerned less with immediate learning outcomes than with sustained learner qualities such as agency, motivation, metacognition, and wellbeing. Taken together, the papers suggest that the question worth asking is shifting from what work technology can do to what human work we must do to make technology worth it. We offer the issue not as a set of resolutions but as an invitation to ask better questions.
ChatGPT has gained significant attention in computer programming education due to its advanced capabilities in assisting coding processes and its growing impact on teaching and learning. Despite rapid technological progress and widespread adoption, further research is required to optimise its integration into programming education. This scoping review used the PRISMA–ScR framework to analyse 59 research articles published between 2022 and 2025. The review identified major research areas related to ChatGPT’s use in programming education, including its role as a programming assistant, automated assessment and feedback, student and educator perceptions, curriculum design and instructional strategies, learning outcomes and performance, ethical and academic integrity considerations and applications across specific programming domains. It also examined methodological approaches, participant demographics and geographical distribution across the included studies. Findings highlight benefits of integrating ChatGPT, including enhanced student engagement, increased accessibility, support for bridging knowledge gaps and assistance with code optimisation. Meanwhile, challenges include risks of overreliance, reduced critical thinking, accuracy limitations and academic integrity concerns. This review provides practical insights for educators, universities, students and researchers. It emphasises using ChatGPT as a learning assistant, implementing clear policies, tailoring artificial intelligence (AI) tools to diverse student needs and guiding future research on effective and ethical AI-driven programming education. Implications for practice or policy: ChatGPT should support debugging, exploration and collaboration rather than code generation. Students must annotate AI outputs, reinforced by oral exams and reflective journals. Educators should blend AI feedback with human evaluation through scaffolded, authentic assessments. Institutions need clear ethical policies, equitable access and staff training. Researchers should use longitudinal, mixed methods studies, while developers design explainable, adaptive and integrity-focused features aligned with course progression.
Digital transformation is reshaping vocational education, creating an urgent demand for teachers' digital literacy, particularly the integration of digital tools and alignment with evolving industry standards. Studies, however, often lack theoretical integration of pedagogical, technological, and industry components; robust longitudinal validation; and contextualized insights into implementation. To address these gaps, an integrated framework was developed by synthesising the technological pedagogical content knowledge (TPACK) framework with industry–education integration principles. A quasi-experimental pretest-post-test matched-group design, complemented by systematic quantitative analyses, was implemented at two comparable provincial vocational colleges in Central China, involving stratified random sampling of 115 full-time teachers per group. Baseline equivalence across all dimensions was confirmed. Following a 10-week intervention, substantial improvements were observed in the experimental group, particularly in industry–pedagogical content knowledge and digital–collaborative content knowledge. Multiple regression analyses further demonstrated robust intervention effects after controlling for demographic variables, with significant interactions between intervention and teacher characteristics. The study contributes threefold: theoretically, by advancing TPACK through industry–education integration framework, establishing longitudinal methodological foundations, and enriching contextual understanding; practically, by providing evaluation tools, modular training guidelines, and localised implementation strategies; and internationally, by demonstrating transferability across institutional arrangements, digital maturity levels, and resource conditions. Implications for practice or policy: Vocational education leaders can enhance teachers' digital literacy by implementing a four-layer integrated evaluation framework aligned with industry standards. Faculty developers may support professional growth through modular training pathways integrating digital tools with industry applications. Instructional designers can develop subject-specific digital resources leveraging industry–education integration principles. Institutional policymakers can foster sustainable development by establishing digital collaboration platforms locally.
Podcasting is increasingly integrated into medical education and formal curricula. Although creative podcasting has been successfully applied in various contexts, the use of podcasts in medical education has primarily been substitutional and supplementary. Creative podcasting aligns with problem-based learning principles by transforming students from passive recipients into active learners. This preliminary co-design study explored medical students' perspectives on using creative, student-produced podcasts to enhance collaborative competencies and their preferences for designing a new educational intervention. The study, approved by Aalborg University's Ethics Committee, employed semi-structured focus group interviews with medical students to co-design a podcast-based intervention. Data were analysed using Braun and Clarke's reflexive thematic analysis, guided by the activity-centred analysis and design (model. Students emphasised the need for clear guidelines, process orientation and methodological variation (epistemic design); suitable environments, accessible technology and supervisor support (set design), as well as interpersonal interactions and optimal group composition (social design). Co-designing with students aligns with existing literature and offers valuable insights for tailoring technology-enhanced educational interventions to local contexts.
As universities seek to diversify and grow online learner cohorts, effective learning design has become increasingly important. Learning design requires multifunctional, interdisciplinary teams of academic and professional staff to bring together pedagogical, content and technological expertise. Few studies have explored the dynamics of such teams and the nature of their collaboration. This article draws on data from seven qualitative case studies across six universities in the United Kingdom, involving academics collaborating with digital learning professionals to design online learning environments. The study is based on 31 interviews and non-participant observations of design team meetings. Our findings highlight how working relationships and role enactments are contingent on individual dispositions, team composition and structural conditions. Meaningful collaboration emerges not from predefined models but through adaptive relational work that supports trust, openness and knowledge integration. The study offers practical and conceptual implications for educators, teaching teams, digital learning teams, researchers and university leadership. Implications for practice or policy: center dot When establishing an interdisciplinary design team, leaders should assess the core and additional expertise available among members to help configure roles, responsibilities and expectations. center dot Universities should support teams in developing a shared understanding of collaboration through structured dialogue and reflective practice. center dot Some flexibility in role boundaries allows team members to fill gaps, provide beneficial redundancy, extend their expertise or challenge themselves. center dot Generative design teams create an environment of mutual support which enables professional learning.
Just over 3 years after the public release of ChatGPT, we revisit the initial research agenda that the then lead editors of AJET outlined in early 2023, and we explore how the five key research areas related to generative artificial intelligence (AI) they identified at the time have been addressed since: sensemaking, assessment integrity, assessment redesign, learning and teaching with AI, and ethics. Significant progress has been made across these areas, evidenced by tailored policy frameworks, sector-wide collaborations and an increasing number of empirical studies. However, given this proliferation of research activity and focus on generative AI, we ultimately ask the question of whether we are reaching saturation point in some areas of generative AI-related research. Drawing on submission trends, we reflect on the value and limits of certain types of empirical evidence within the educational technology field, and tertiary education more generally. Rather than proposing fixed saturation criteria, we call here for reflection and dialogue, for researchers, journal editors and publishers. We argue that while we may not have reached saturation point yet, we seem to be getting close to it in some focus areas and contexts.
Generative artificial intelligence (GenAI) poses unprecedented challenges and opportunities for assessment in universities. Existing studies that explore students' adoption of GenAI in assessment show mixed and, to some extent, contradictory findings. Some studies have found optimistic views on GenAI, while others have highlighted significant concerns among students. This study aimed to explore students' interactions with GenAI in completing non-exam assessments using a socio-technical view that recognises the sociocultural and technological factors influencing students' behaviours. We sampled three teacher education courses that sought to embed the use of GenAI in the assessment. A mixed-methods approach was adopted, which involved data collected from a survey (N = 85), student interviews (N = 11), course materials and a declaration of GenAI use in students' submitted assignments (N = 158). Our findings indicate that approximately two-thirds of the students decided not to adopt GenAI when allowed, and that the assessment design, the perceived value of the assessment, students' self-confidence and concerns about being wrongly accused of plagiarism were the most frequently cited reasons. This study shows the importance of consistent assessment policies and effective communication. Moreover, it is important for instructors to have a programme-level view when designing GenAIrelated assessment policies.
Inquiry-based learning (IBL) is a problem-driven and exploration-centred learning method. The emergence of large language models (LLMs) such as ChatGPT provides a new interactive environment for IBL. However, research has not sufficiently explored how students interact with LLMs for IBL. This study aimed to understand students' behaviours interacting with LLM at different cognitive levels during the IBL process. We conducted an experiment on a data science academic writing task and used Bloom's educational taxonomy to examine the behavioural patterns of students' IBL at different cognitive stages. Through the exploratory thematic analysis of 117 interview transcripts, 370 interaction records and 1,694 minutes of screen recordings, we identified 14 interaction patterns among students at different levels of prior knowledge. This article discusses the potential impact of self-efficacy and metacognitive monitoring on students' learning behaviour in an LLM-driven learning environment and called for the design of a guiding planning framework and scaffolding to address challenges such as reliance on artificial intelligence. Our study provides new insights for the development of IBL in the era of emerging artificial intelligence technologies. Implications for practice or policy: center dot Educators can improve student inquiry-based learning outcomes by designing cognitive scaffolding that targets specific higher-order thinking stages. center dot Instructional designers should develop planning frameworks that mitigate overreliance on artificial intelligence while fostering student metacognitive monitoring. center dot Policymakers could implement training programmes to enhance students' critical evaluation skills within an LLM-driven environment.
Estimating the volume of student learning in courses is more of an art than a science. Yet, mismatches between advertised workload and actual student effort can lead to high student dropout rates. To investigate the factors affecting student workload and whether they can be quantified, we undertook a mixed methods study aimed at refining and testing a student workload calculator specifically tailored for online micro-credentials. Utilising a case study methodology, we blended a literature review with our experiential knowledge as learning designers to refine existing workload calculators into a cohesive reflective tool to interrogate assumptions about learner effort and time on task. We then employed quantitative methods to test the calculator against advertised workloads in a sample of online micro-credentials from one platform. Findings suggest a potential discrepancy between provider-advertised workloads and calculator-based workload estimates, indicating that commonly used advertising practices may rely on materially different assumptions about student effort than those of conservative, research-informed modelling approaches. While this study did not measure actual student time on task, the consistency and magnitude of this discrepancy warrant closer scrutiny of how workloads are estimated and communicated to learners, as misalignment could contribute to student disengagement, lower completion rates and reduced trust in online learning. Implications for practice: center dot Course designers should take extra care when estimating workloads for online micro-credentials. center dot Education providers could balance transparency and appeal by offering both typical and maximum workload estimates. center dot Activity time estimates should be framed as flexible guides to support student planning. center dot Educators should use workload calculators as reflective tools rather than fixed measures.
In order to determine the effectiveness of virtual reality (VR) training and reliability for mass utilisation in competency-based training in the construction industry, we collected data related to learning outcomes (assessment scores and recall, immediately and after 1 month) from 109 participants (n = 59, VR group; n = 50, non-VR group) from three registered training organisations in south-east Queensland. Interviews were also conducted with 48 of the participants in the VR group. One month after completion of training (VR or non-VR), participants were sent a follow-up survey to assess recall. Our findings showed that the VR environment is as effective as non-VR training for specific learning outcomes immediately as well as after 1 month. Participants identified features that differentiated their learning experience when using the VR environment including the importance of the provision of a safe and secure learning environment as preparation for future learning. This research has implications for the use of advanced technology to support competency-based training in the construction industry as well more broadly. Implications for practice or policy: center dot VR can be used effectively as part of an approach to competency-based training. center dot Course leaders should consider the benefits of VR training beyond learning outcomes-in particular in providing a safe and secure learning environment for subject matter that involves physical safety issues in real life. center dot Course designers may need to consider how VR could complement traditional training to scale up construction skills training.
This study investigated the evolving role of artificial intelligence (AI) in higher education by analysing learner-generated questions through a constructivist framework. Drawing on Piaget and Vygotsky's theories, student inquiries were categorised into three roles: knowledge transmitter, facilitator and co-learner. Data from 11 students across 12 information technology courses yielded 434 authentic questions, expert labelled and augmented to balance class distributions. Several natural language processing models including bidirectional encoder representations from transformers (BERT; baseline and finetuned), disentangled attention BERT approach (DeBERTa) and robustly optimised BERT approach (RoBERTa) were evaluated for their ability to classify these questions. Results indicate that while models excel at processing factual (knowledge transmitter) queries, they face challenges distinguishing higher-order facilitator and co-learner questions. Notably, DeBERTa achieved the highest overall accuracy (86.36%) yet struggled with capturing contextual nuances inherent in complex queries. These findings underscore the potential of AI to support personalised learning and adaptive feedback in educational settings while highlighting the indispensable role of human oversight. Implications for integrating such models into learning management systems and avenues for future research including model refinement, cross-disciplinary validation and ethical AI implementation are discussed. Implications for practice or policy: center dot Instructors could enhance learner engagement by integrating AI-based question analysis tools to provide tailored feedback based on inquiry depth. center dot Course designers may need to incorporate AI-driven scaffolding strategies to support students' higher-order thinking skills. center dot Learning management systems could benefit from embedding automated question categorisation functions to identify students' learning needs more efficiently. center dot Educational institutions should consider developing ethical guidelines for the use of AI in formative assessment processes.
This study examined the effects of integrating artificial intelligence (AI) tools into informal digital learning of English (IDLE) to enhance cognitive and non-cognitive skills, as well as listening and speaking proficiency among English as a Foreign Language students. A sample of 120 Egyptian university students participated in a mixed-methods design that consisted of a questionnaire, pretests and post-tests for listening and speaking skills and semi-structured interviews. Quantitative data were analysed using descriptive statistics, t tests and mixed analysis of variance, while qualitative responses were thematically explored. The findings revealed significant advancements in cognitive skills, including the regulation of attitudinal needs, goal commitment, resource allocation and metacognitive skills, as well as enhanced non-cognitive skills. However, social connections via AI were found to be less impactful, with many students reporting limited authentic interactions. While AI-driven IDLE significantly enhanced speaking proficiency, listening skills showed more modest gains, suggesting differential effects of AI on productive versus receptive skills. Despite technical challenges, AI-based IDLE demonstrated potential for personalising learning. Future research should address these challenges while focusing on bridging the gap between informal digital learning and real-world language use. Implications for practice or policy: center dot Educators should integrate AI tools into blended learning models, combining AI-driven practice with real-world communicative opportunities to bridge the gap between simulations and authentic language use. center dot Developers must prioritise customisation in AI tools, such as adaptive learning paths and realistic conversation practice, to address diverse learner needs effectively. center dot Policymakers and administrators should invest in resolving technical barriers (e.g., speech recognition accuracy, Internet reliability) to optimise AI tool effectiveness and user experience.
This is the fifth in a series of editorials that have reflected on and celebrated the past 40 years of the Australasian Journal of Educational Technology (AJET). In this editorial we look back on the most recent 20 years of AJET and identify some of the key themes in AJET articles during that period. Overall, eight key themes are identified: 1) AI and automation in learning, 2) Assessment and feedback, 3) Equity, inclusion and ethics, 4) Learning analytics and data-informed insight, 5) Pedagogical integration, 6) Professional learning and academic development, 7) Self-regulated learning, and 8) Technology adoption. These themes were identified for the period 2008-2025, where we have used AI to assist in our analysis. The themes for the period 2005-2007 were identified manually through abstracts, titles, introductions, and conclusions. Overall, AJET has for the last 40 years provided a significant forum to discuss educational technologies and resulting innovations. Through it all, what has shone through over the past 40 years is a passion for evidence-based practice that provides the best learning outcomes for learners, initially across the whole education sector, and more recently with a specific focus on tertiary education.
Remote education is an alternative delivery modality for digital environments, responding to several societal requirements and the needs of today’s education. Although the term has been used since the early 2000s, the COVID-19 pandemic forced it to emerge as a temporary alternative to continue the educational processes during confinement. However, the research and interest have persisted. This research analysed the remote experiences reported in the literature, emphasising the teaching and learning strategies and the technologies used to support them. A systematic literature review using the PRISMA protocol identified 51 articles from the Scopus and Web of Science indexing databases. The analysis and synthesis involved categorisation and classification guided by research questions directed towards the review objective. The findings revealed that (a) remote experiences use diverse strategies, predominantly active methodologies that privilege the experiential; (b) technologies support the remote strategy development, but the use or intention of the technology is not always straightforward; (c) some studies note strategies mediated by remote and cyber-physical labs, artificial intelligence and teaching assistants; (d) general interest in the topic exists in all areas, disciplines and educational levels, predominantly higher education; and (e) it is necessary to follow up on remote experiences after pandemics. This research adds value through its observations that, more than an emergency alternative, remote education is a pillar of hybrid education and future education. Implications for practice: Faculty designed remote experiences based on active methodologies and enabled by technologies, which impacted learning. This suggests that it is possible to carry out a quality educational process in remote. The COVID-19 pandemic accelerated the transformation of education in the multimodal forms of delivering learning experiences to meet the needs of contemporary society. Universities recognise the potential of remote learning to address issues of access and reach, while also enhancing hybrid and flexible learning models.
Design-based research is a methodological approach that has been applied in the field of educational technology since the beginning of this century. The main aim of this article was to explore its use in the field of educational research, specifically in the context of higher education during the 5-year period from 2019 to 2023, coinciding with the COVID-19 pandemic and the technological efforts made in response to it. In the current work, 180 documents were analysed after a PRISMA selection procedure. Although the analysis covered documents from a variety of continents, Europe (n = 73) and America (n = 45) are the geographical areas that appear most frequently. Likewise, Social and Legal Science (n = 87) are the knowledge areas in which design-based research is most widely used, followed by Engineering and Architecture (n = 28) and Health Sciences (n = 21). In more qualitative terms, the findings of this work provide important information about the diverse use of this research approach, the data collection processes used and the quality criteria and design principles that appear explicitly in the documents analysed. Implications for practice or policy: Instructional designers can observe examples of educational designs in different content areas. Researchers can find a theoretical basis for building research projects aimed at designing educational artefacts in a wide range of scientific areas. Policymakers can identify successful practices in other institutions to improve the management of their resources. Researchers can access emerging trends and best practices in higher education, helping to update teachers’ skills.
The advent of artificial intelligence (AI) has sparked significant debate regarding optimal implementation strategies, with many discussions relying on assumptions that have not been thoroughly tested. This study aims to move beyond speculation by critically examining the role of AI tools, specifically ChatGPT PDF, in supporting self-regulated learning (SRL) in the context of academic literacy. Situated within the process of composing a master's thesis in education in Australia, this investigation adopted a qualitative, self-reflective research design to compare the effectiveness of AI tools with traditional university-based academic support models. The findings suggest that while AI tools like ChatGPT PDF can enhance SRL through real-time feedback and increased accessibility, they also have limitations in providing deeper cognitive support. To optimise their effectiveness, AI tools should be integrated within a comprehensive framework that promotes self-efficacy, metacognitive reflection and a deeper understanding of academic literacy. This approach ensures that AI tools not only aid task completion but also foster transformative learning and independent thinking.
In this editorial, which continues the series celebrating the 40th anniversary of the Australasian Journal of Educational Technology (AJET), we explore AJET's first 10 years from 1985 to 1994. We trace the origins and evolution of AJET, from its establishment by the Australian Society for Educational Technology (ASET) to its current open access format supported by the Australasian Society for Computers in Learning in Tertiary Education (ASCILITE). The emergence of the journal came at a time of significant changes in the Australian education sector and it is in this context we analyse the content of early AJET articles, highlighting key themes such as the definition of educational technology, the evolving role of educational technologists, the concept of computer literacy, and early discussions on artificial intelligence. Finally, we reflect on the enduring questions regarding technology's impact on learning and the continuing relevance of AJET in a changing educational technology landscape.
