2026-10-05
Feifei Han, Chris Deneen, Henk Huijser, Linda Corrin
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.
2026-09-16
Sharon Bee Wah Tan, Yuanli Zheng
Novice learners often struggle to engage with the complexity inherent in systems thinking. This study explores how a customised generative artificial intelligence (GenAI) chatbot can scaffold students’ systems thinking processes in a critical thinking and problem-solving skills module at an institute of higher education. Adopting a convergent mixed methods design, the study involved 52 students who interacted with the chatbot and completed an assignment addressing complex sustainability issues. Data from chatbot logs, reflection journals and assignments were analysed using rubrics that scored interaction depth and learning quality. Linear regression revealed a significant relationship between the depth of chatbot interaction and assignment quality. Building on these quantitative scores, content analysis of the chat logs was conducted to identify distinctive behaviours associated with each interaction level. For example, students with deep-level interactions often engaged in iterative questioning and elaboration, while those with moderate-level interactions sought feedback but did not always pursue further dialogue. Finally, interviews with six students provided insight into the factors shaping their interaction patterns, including how they perceived the role of the chatbot and their sense of agency. Implications for practice or policy: Educators could model effective GenAI chatbot use by emphasising the intended pedagogical purpose of GenAI tools. Instructional designers could design complementary classroom activities to support diverse learners and extend GenAI-mediated learning.
2026-09-16
Hiep-Hung Pham, Thai-Binh-An Nguyen, Cam-Tu Vu, Ha-Giang Tran-Thi, Thanh-Thao Phan Thi, Phuong-Thuc Doan Thi, Linh-Chi Nguyen
As digitalisation continues to reshape education and massive open online courses (MOOCs) gain popularity, micro-learning has emerged as a key strategy to enhance learning in both formal and informal settings. Recognising this potential, the present study investigated Vietnamese learners’ psychological needs, engagement, satisfaction, perceived performance and loyalty within the context of MOOCs – courses designed around micro-learning principles. Through partial least squares structural equation modelling, the findings revealed that autonomy and competence significantly enhanced learner engagement, while relatedness showed no substantial effect. Engagement, in turn, strongly predicted satisfaction, which subsequently influenced both loyalty and perceived performance. Our model accounted for high levels of variance in all endogenous constructs, underscoring the theoretical robustness of self-determination theory in digital learning contexts. This research highlights the critical role of intrinsic motivation and satisfaction in shaping learner behaviour, offering practical implications for instructional designers and educational institutions seeking to enhance micro-learning experiences and foster long-term learner retention and loyalty. Implications for practice or policy: MOOC providers should boost engagement by integrating self-paced, interactive and case-based learning into micro-learning environments. Educators should use tools like discussion boards and collaborative tasks to enhance learner-to-learner engagement, addressing the limited role of relatedness. Educational institutions and policymakers could support formalising micro-learning by embedding it as a core component of full-time hybrid curricula and interleaving it across all online and distance learning programmes.
2026-09-16
Mahmut Demir, Dilek Ünveren
This study investigated the relationship between perceived artificial intelligence (AI) competency and critical thinking skills among Generation Z students, with a focus on the mediating roles of digital literacy and cognitive engagement. Grounded in social cognitive theory, the research posited that individuals who perceive themselves as competent in AI are more likely to engage deeply with digital tools and learning processes, thereby enhancing their critical thinking abilities. Data were collected from 464 university students in Türkiye using validated self-report measures. Structural equation modelling and bootstrapped mediation analyses revealed that both digital literacy and cognitive engagement significantly mediate the link between AI competency and critical thinking. Notably, a serial mediation pathway was also confirmed, highlighting how perceived AI competency fosters digital literacy, which in turn promotes cognitive engagement and ultimately strengthens critical thinking skills. These findings offer theoretical and practical insights into how AI-related self-efficacy supports higher-order cognitive processes in digitally mediated educational settings. Implications for practice or policy: Curriculum designers should integrate AI literacy across disciplines to enhance students’ critical thinking skills. Instructors can enhance engagement by using inquiry-based tasks that require students to actively use AI tools. University leaders can enhance outcomes by incorporating comprehensive digital literacy training into their programmes. Policymakers should address equity gaps by funding initiatives that promote AI literacy and digital inclusion. Faculty developers could prioritise training educators to model ethical and reflective AI practices.
2026-09-16
Yixuan Chen, Changqin Huang, Zhongmei Han, Di Zhang, Yiheng Lou, Yihua Zhong
Personalised generative artificial intelligence (GenAI) feedback holds potential for supporting students’ creative problem-solving, yet empirical evidence on its effects and underlying mechanisms remains limited. This study addressed these gaps through a within-subjects experiment with 62 Chinese university students, aiming to investigate the effects of personalised GenAI feedback on students’ creative problem-solving and feedback uptake in comparison with standard GenAI feedback. To further reveal the mechanisms underpinning these effects, we examined how students’ interactions and behavioural uptake of feedback during human–AI collaboration were associated with their creative problem-solving performance. Results showed that personalised GenAI feedback improved the quality and elaboration dimensions of students’ solutions, while slightly reducing the amount of ignored feedback compared with the standard GenAI condition. Moreover, higher-level uptake of GenAI feedback, reflected in the amount of applied feedback, positively predicted creative problem-solving performance across conditions, and interaction behaviours such as co-constructing ideas and negotiating inconsistencies with AI were consistently associated with this high-level uptake. This study offers insights for designing GenAI feedback that emphasises personalisation to foster students’ creative engagement, underscoring the critical role of students’ interactive collaboration with AI-generated feedback in cultivating innovation within GenAI-supported learning environments. Implications for practice or policy: When designing personalised agents, AI developers should integrate complementary feedback mechanisms to enhance both student engagement and creative development. Educators should prioritise fostering students’ higher-level uptake of GenAI feedback, as this is critical to its effectiveness on creative production. Instructional approaches should be designed to cultivate students’ interactive engagement with GenAI tools and develop their creative capacities, thereby fostering productive human–AI collaboration.
2026-09-16
Xin Li, Yue Zhang, Tingyu Liu
The rapid integration of generative artificial intelligence (AI) into peer assessment is reshaping feedback practices in higher education. However, empirical evidence directly comparing human and AI-generated feedback remains limited. This mixed methods study compared feedback quality and student perceptions between 13 undergraduate peer groups comprising 39 students and ChatGPT-4o in a smart learning environment design course. Quantitative analyses indicated no significant difference during the first assessment stage; however, in the second stage, ChatGPT-4o provided feedback that was significantly higher in constructiveness, accuracy and concreteness. Thematic analysis further revealed that students perceived group feedback as contextually rich but sometimes subjective, whereas ChatGPT-4o feedback was viewed as more objective and well structured, though occasionally lacking contextual sensitivity. Overall, students demonstrated high levels of acceptance towards both feedback sources, but high-acceptance groups made more effective use of AI feedback during revision, resulting in higher accuracy scores. These findings highlight the complementary roles of human and AI feedback in strengthening the formative value of peer assessment and provide implications for the design of AI-augmented feedback systems in higher education. Implications for practice or policy: Clear rubrics and explicit guidance for AI use are essential to improve the quality, consistency,and transparency of peer assessment. Combining peer group feedback with AI-generated feedback can enhance formative assessment by integrating contextualised human insights with structured and objective AI support. AI-generated feedback should be used as a supplementary tool rather than a standalone assessor, with ongoing human oversight to address potential inaccuracies and ensure pedagogical quality.
2026-09-16
Nguyen Quyen Anh Tran, Ngoc Nguyen Nguyen
The growth of technology-mediated online learning has created new challenges for classroom management, particularly in English as a Foreign Language contexts, where interaction and participation are central to learning. Concerns have emerged that traditional strategies, such as maintaining eye contact and using physical proximity, are rendered ineffective. Adopting a sequential mixed methods design, this study examined whether novice and experienced teachers’ classroom management beliefs differ and how these beliefs are enacted through technology-mediated practices. Survey findings from 135 Vietnamese English as a Foreign Language lecturers revealed no statistically significant differences in beliefs. However, interviews with 10 participants revealed notable differences in practice. Novice teachers reported broader use of technological affordances to support pre-class preparation, participation monitoring, instructional activities and behavioural management. Experienced teachers adopted technology more selectively and emphasised facilitative instructional approaches through teacher presence and learner autonomy. While technological affordances were closely associated with instructional and behavioural management, relational management relied primarily on interpersonal communication. The findings suggest that teachers’ technology use reflects underlying pedagogical orientations and that the role of technology differs across classroom management dimensions. Implications for practice or policy: Training programmes for technology-mediated classroom management should consider how pedagogical approaches and teaching experience may influence teachers’ use of technological affordances. When selecting digital tools, teachers should prioritise how the technology supports instructional and behavioural management rather than the features of the tool itself. Teachers should recognise that effective technology use does not replace the need to practise relational management, which is built through trust, care and interpersonal interaction with students.
2026-09-10
Richard McInnes, Laura Airey, Paul Moss, Sasikala Rathnappulige
Ease of use and access are leading educators to adopt generative artificial intelligence (GenAI) technologies to augment their practice in areas such as constructive alignment. Drawing on critical and post-digital literature to frame a critical discourse analysis, we interrogated grey literature on utilising GenAI in the constructive alignment process. We argue that while the current GenAI discourse promises efficiencies, the encroachment of automation into pedagogical design risks devaluing the educational process, undermining educator expertise and shifting constructive alignment towards an instrument of compliance. As such, educators must reclaim constructive alignment as a reflective, human-centred practice to safeguard pedagogical depth, improve design robustness and ensure that teaching remains a meaningful and enjoyable professional endeavour. Further, we contend that for GenAI to defensibly augment constructive alignment, it must transition from generic, Internet-trained assistance towards institutionally bounded systems configured around situated pedagogy, policy and institutional quality standards. By positioning GenAI as a reflective partner rather than an automated substitute, institutions can preserve educator agency and professional judgement. To support this shift, we also call for a repositioning of educational developer roles away from narratives of technology implementers back towards active collaborators in constructive alignment processes. Implications for practice: Educators should critically engage with constructive alignment rather than automating it through GenAI to preserve pedagogical depth and meaning. Educators can strengthen learning design by using GenAI as a reflective prompt, not as a shortcut for efficiency. Educational developers need to assert their role as pedagogical collaborators rather than technology implementers. Policymakers and administrators should promote frameworks that value educator expertise and contextualised learning design over standardised automation.
2026-09-10
Xi Fang, Liangjie Zhu, Shuang Xin
Research lacks a comprehensive analysis of the factors influencing students’ continuous usage of learning management system (LMS) mobile applications, especially considering the heterogeneity and dynamic psychological changes among undergraduate students. This study employed an integrative stimulus-organism-response model and complexity theory for exploring the complex and dynamic processes that lead to students’ continuous usage intentions, based on their perceptions of app quality attributes and emotional responses. Using fuzzy-set qualitative comparative analysis on survey data collected from 400 undergraduate students in China, this study identified six distinct configurations that encompassed various combinations of app quality attributes and emotional responses, which may contribute to students’ intention to continue using LMS apps. The findings highlight the complex and dynamic nature of students’ reactions to LMS apps in the context of higher education. It offers significant implications for higher education educators, enabling them to better understand their students and enhance students’ intentions to continuously use LMS apps in their learning activities. Implications for practice or policy: LMS operators should comprehensively address both app quality attributes and students’ affective responses to increase students’ continuous usage intentions. Instructors should endeavour to elicit students’ affective responses to LMS applications through various integrated course activities. Instructors should focus on improving their attitudes, increasing student interactions and encouraging students’ feelings of autonomy.
2026-08-21
Qi Xia, Zili Zhang, Taiqi Xing, Andic Branko, Thomas K.F. Chiu
As artificial intelligence (AI) becomes increasingly embedded in higher education, it is simultaneously transforming how interdisciplinary education is conceived, delivered and evaluated. This study presents a systematic review of 59 recent studies to investigate the evolving relationship between AI and interdisciplinary education in higher education. Guided by four research questions, we examined (a) the forms of interdisciplinary education emerging in the AI context; (b) the main research focuses within this intersection; (c) the functional roles AI plays in interdisciplinary settings; and (d) the impact of AI on different educational stakeholders. Findings indicate that AI-supported interdisciplinary education includes the integration of science, technology, engineering and mathematics (STEM) and science, technology, engineering, art and mathematics (STEAM); non-STEM applications; cross-disciplinary curricula; and diverse student backgrounds. The literature search identified three primary research focuses: AI system and tool development, AI-empowered curriculum transformation and AI-based learning analysis and evaluation. Functionally, AI acts as an evaluator, agent, monitor and assistant, reshaping how knowledge is constructed and assessed across disciplines. Finally, this study developed an interdisciplinary human-AI interactive learning model, providing a conceptual reference framework for AI-supported interdisciplinary teaching and outlining five directions for future research. This review offers critical insights for researchers, educators and policymakers seeking to navigate the challenges and opportunities at the intersection of AI and interdisciplinary higher education. Implications for practice or policy: Course instructors can embed AI-supported activities that promote interdisciplinary problem-solving. Institutions should establish policies on AI ethics, bias, transparency and student data privacy. Educators need sustained training to build practical AI literacy. Policymakers should ensure equitable access to AI tools for all learners. Programmes should align AI use with sustainability and global citizenship goals.
2026-07-10
Eloise Thomson, Hayden Park, Rachel Chapman, Sarah Louise Nelson
As generative artificial intelligence (GenAI) tools proliferate in higher education, critical questions arise about their effectiveness in providing assessment feedback, particularly in professional preparation contexts where feedback shapes future practice. This mixed methods study examined the quality and student experience of GenAI-generated versus human feedback among 15 first-year pre-service teachers. Using ChatGPT-4 to produce rubric-aligned feedback compared with expert human assessment, the results showed GenAI consistently awarded higher marks and revealed a “praise paradox”, where its uniformly positive tone undermined educational value by appearing excessive and developmentally limited. Student surveys showed a strong preference for human feedback across all quality dimensions, especially accuracy, actionability and professional contextualisation. Key themes included superficial praise concealing learning gaps, reliance on pedagogical expertise over algorithmic judgement and confusion from score disparities. While GenAI demonstrated strengths in personalisation and positivity, it lacked evaluative judgement and contextual understanding essential for teacher education. The study concludes that GenAI should complement, not replace, human expertise, advocating a human-in-the-loop model that preserves the relational and developmental dimensions critical to effective feedback in professional preparation programmes. Implications for practice or policy: Teacher education programmes should not use GenAI to replace human feedback in primary assessment. Teacher educators should retain responsibility for evaluative judgement, professional context and developmental guidance. Course leaders may use GenAI to support formative feedback drafting, but only with human review and refinement. Institutions should establish transparent policies and professional learning so GenAI supports, rather than substitutes, relational feedback practice.
2026-02-26
Hyunmin Lee, Amara Atif, Kyeong Kang
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 fine-tuned), 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: Instructors could enhance learner engagement by integrating AI-based question analysis tools to provide tailored feedback based on inquiry depth. Course designers may need to incorporate AI-driven scaffolding strategies to support students' higher-order thinking skills. Learning management systems could benefit from embedding automated question categorisation functions to identify students' learning needs more efficiently. Educational institutions should consider developing ethical guidelines for the use of AI in formative assessment processes.
2026-02-18
Amira Ali
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: 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. Developers must prioritise customisation in AI tools, such as adaptive learning paths and realistic conversation practice, to address diverse learner needs effectively. Policymakers and administrators should invest in resolving technical barriers (e.g., speech recognition accuracy, Internet reliability) to optimise AI tool effectiveness and user experience.