2025: ASCILITE 2025 Conference Proceedings

Generative AI tools such as ChatGPT, Microsoft Copilot and Gemini have often been promoted in negative ways as they may be used by students in an attempt to breach academic integrity. One issue with assessment question pools is that they

Generative AI tools such as ChatGPT, Microsoft Copilot and Gemini have often been promoted in negative ways as they may be used by students in an attempt to breach academic integrity. One issue with assessment question pools is that they are often set and not updated frequently. Hence, they can be overused and are vulnerable to malicious use such as: being sold for money or ported to the dark web and therefore their practical value for assessment is significantly diminished. There is another side of gen AI that can assist academics in authoring assessment questions that is substantially different to the standard question pools that academics often rely on. This paper will illustrate potential uses, benefits, and precautions that need to be taken when using gen AI for assessment design.

In an era of increasingly asynchronous and self-regulated professional learning, understanding what drives engagement and achievement is critical. This study explores the impact of interactive video content on learner engagement and

In an era of increasingly asynchronous and self-regulated professional learning, understanding what drives engagement and achievement is critical. This study explores the impact of interactive video content on learner engagement and academic performance in the capstone unit of a Graduate Certificate in Learning and Teaching (Higher Education). Introduced in 2024, the video-based innovation aimed to streamline the learning experience for a growing cohort of mature-age, success-oriented educators balancing multiple professional and personal commitments. Drawing on learning analytics from Canvas LMS and EchoVideo, the study investigates how video views, quiz completions, and other digital behaviours correlate with overall course grades. Findings reveal that video engagement—particularly viewing and interacting with quizzes—has a stronger positive correlation with academic success than traditional metrics like page views or participations. Notably, 58.5% of learners watched at least one video, and 48.8% completed at least one quiz, suggesting meaningful uptake. These insights underscore the value of well-designed, feedback-rich video content in fostering deeper engagement and improved outcomes in professional learning contexts. The study offers timely recommendations for designing scalable capstones responsive to the complex realities of today’s higher education professionals.

The integration of Artificial Intelligence (AI) into Kenya's Competency-Based Curriculum (CBC) presents both transformative opportunities and complex challenges. This study examines the ethical, infrastructural, and pedagogical

The integration of Artificial Intelligence (AI) into Kenya's Competency-Based Curriculum (CBC) presents both transformative opportunities and complex challenges. This study examines the ethical, infrastructural, and pedagogical implications of AI adoption—focusing on generative and language-based models—in Kenya's education sector, with a particular focus on teacher digital readiness, data governance, and linguistic inclusion. Employing a multi-method qualitative approach, the research combines policy analysis with semi-structured interviews of ten experts, including five public school teachers and five government officials. The findings reveal systemic barriers, including inadequate professional development, fragmented data management frameworks, and the marginalisation of Indigenous languages in AI tools. In response, the study proposes a context-responsive, human-centred model grounded in five pillars: policy and infrastructure reform, teacher capacity-building, local language and cultural integration, participatory design, and equitable learning analytics. This model provides a roadmap for ethically and inclusively integrating AI into CBC implementation, aligning with Kenya's broader goals of equity, innovation, and digital transformation in education.

Virtual Reality (VR) is gaining recognition as a valuable tool in initial teacher education (ITE), particularly for supporting classroom practices. This study explores how synchronous, shared VR experiences, using 360-degree video of real

Virtual Reality (VR) is gaining recognition as a valuable tool in initial teacher education (ITE), particularly for supporting classroom practices. This study explores how synchronous, shared VR experiences, using 360-degree video of real classrooms, may help pre-service teachers identify, evaluate, and plan for inclusivity. Master of Teaching students engaged in pre- and post-surveys, combining Likert-scale and open-text responses, to assess the impact of VR-enhanced learning. Results suggest that VR can effectively complement traditional placements by offering access to diverse teaching styles and classroom dynamics that are not always observable in live settings. The ability to revisit and collaboratively analyse key teaching moments supports deeper professional reflection. Positioned within a reflective, scaffolded framework, VR offers a practical extension to real-world experiences, enhancing teacher preparation for inclusive education. This study contributes to growing research on the role of immersive technologies in teacher training and highlights VR’s potential to support more equitable and student-centred learning environments.

This research investigates online teaching strategies to reduce non-completion of online courses by exploring students' emotional experiences and implementing support interventions grounded in care ethics principles. Using action research

This research investigates online teaching strategies to reduce non-completion of online courses by exploring students' emotional experiences and implementing support interventions grounded in care ethics principles. Using action research across three cycles in an introductory accounting course at an Australian university, this study investigates how teachers can effectively address emotional engagement in online learning to enhance student engagement and improve course completion rates. Student voices gathered through interviews, surveys, and feedback revealed emotional patterns that guided the development of targeted interventions, including personalised communication, time management support and extra assessment preparation sessions. These interventions incorporated four key elements of caring relationships: attention, responsibility, competence, and responsiveness. Data analysis employed thematic analysis of qualitative data, supported by descriptive statistics of engagement metrics and academic performance indicators. Findings demonstrate that care ethics-based interventions significantly improved student engagement, academic outcomes and course completion rates. Students recognised and valued teacher care, confirming that supportive relationships increase online course completion. However, competing life responsibilities hinder students' ability to engage with support interventions. The research establishes that effective online education requires integrated strategies combining caring teacher-student relationships with institutional support to accommodate diverse student circumstances and complex life contexts.

This paper interrogates the tension between institutional deployments of AI chatbots for student wellbeing and the informal, peer-to-peer support networks that emerge in university bathroom spaces. Anchored in the student-inscribed phrase

This paper interrogates the tension between institutional deployments of AI chatbots for student wellbeing and the informal, peer-to-peer support networks that emerge in university bathroom spaces. Anchored in the student-inscribed phrase “ARTIFICIAL UNCONSCIOUSNESS CHATBOT,” the study undertakes a cross-disciplinary synthesis of two bodies of literature: systematic reviews evaluating the effectiveness and limitations of AI chatbots in mental health support, and empirical studies of bathroom graffiti as a mode of collective care. Drawing on Cohen’s (1997) theory of the safe house, Jung’s (1966) wounded healer archetype, and Heidegger’s (1962) phenomenology of authentic being-with, we analyse why graffiti walls succeed in fostering protected vulnerability, temporal spaciousness, and existential co-presence, while institutional AI systems often collapse these conditions into surveillance, efficiency, and artificial intimacy. The analysis culminates in the Human-AI Synergy Model, which reconceptualises AI not as a therapeutic substitute but as a relational relay: a tool that can preserve anonymity, scaffold temporal spaciousness, and reduce cognitive burden while pointing students toward authentic human connection. By contrasting what is systematically erased in institutional AI systems with what flourishes in informal peer safe houses, the paper offers critical guidance for designing AI in higher education that enhances rather than erodes the phenomenological conditions of genuine care.

Artificial Intelligence (AI) is increasingly embedded in daily life and professional practice, offering both transformative potential and ethical challenges. While AI supports efficiency and innovation, it also presents risks, including

Artificial Intelligence (AI) is increasingly embedded in daily life and professional practice, offering both transformative potential and ethical challenges. While AI supports efficiency and innovation, it also presents risks, including bias, misinformation, and limited situational judgment. A recent global survey found that although 86% of university students use AI, many feel unprepared to engage with it critically or ethically. At the University of Sydney, AI is taught across most disciplines, yet gaps remain, particularly in programs like the Master of Clinical Psychology (MCP). As AI continues to reshape healthcare and psychological practice, future clinical psychologists must develop AI literacy to navigate this evolving landscape responsibly. This proposal introduces a framework for embedding AI ethics and practical applications into the MCP curriculum, equipping students with the critical skills needed to engage with AI ethically and effectively in their future roles.

Learning analytics (LA) systems has potential to create inclusive educational environments supporting diverse learners in higher education. However, successful implementation depends on user acceptance by academic staff and administrators

Learning analytics (LA) systems has potential to create inclusive educational environments supporting diverse learners in higher education. However, successful implementation depends on user acceptance by academic staff and administrators. This study examines factors influencing LA adoption in Australian universities through the Technology Acceptance Model (TAM). Using qualitative case study methodology, interviews were conducted with twenty-three participants across five demographically diverse Australian universities. Template analysis examined data according to TAM's core constructs: perceived usefulness, perceived ease of use, attitude towards use, behavioural intention, and actual system usage. Findings reveal that while participants recognised LA's potential for evidence-based decision making and personalised learning support, adoption was hindered by system complexity, data quality issues, and inadequate training. Power users demonstrated LA's capacity to enhance inclusive teaching through tailored interventions for at-risk students. Institutional barriers including fragmented systems, access restrictions, and policy constraints limited widespread adoption. Results suggest successful LA implementation requires user-centred design, comprehensive training, and institutional support structures. These findings thus provide practical insights for universities seeking to leverage learning analytics as inclusive technology promoting equitable educational outcomes.

The ability to communicate across cultures is a crucial skill in our global workforce, especially for English language learners as a lingua franca. As technology becomes central to how learners access language and culture, it is essential

The ability to communicate across cultures is a crucial skill in our global workforce, especially for English language learners as a lingua franca. As technology becomes central to how learners access language and culture, it is essential to examine its role in fostering intercultural communicative competence (ICC). This study investigates how ICC is developed through students’ engagement with technology in an English for Specific Purposes (ESP) context in Indonesia. The study is situated in Indonesia, where the growing demand for global engagement has increased the need for ESP, yet the integration of ICC within ESP remains limited. Drawing on established ICC models and processes, six Indonesian ESP students participated by documenting their daily use of English through a Day in the Life (DITL) video and semi-structured interviews. Reflexive thematic analysis (TA) generated two key themes: technology as a bridge for cultural sharing and learning about cultures through digital communication. These findings illustrate how students navigate and blend formal instruction with informal, self-directed digital practices to develop ICC. The analysis also reveals tensions between surface-level cultural exposure and deeper intercultural competence development. Implications for ESP pedagogy include encouraging the use of digital tools beyond the classroom, fostering reflection on intercultural experiences, and creating opportunities for real-world communication that challenge native-speaker ideologies.

The Australian tertiary education sector is undergoing significant transformation, driven by evolving workforce demands, technological disruption, and the imperative for inclusive, future-focused learning environments. In response to

The Australian tertiary education sector is undergoing significant transformation, driven by evolving workforce demands, technological disruption, and the imperative for inclusive, future-focused learning environments. In response to national policy directions, this paper presents the co-design and implementation of Swinburne University of Technology’s Educator Capability Framework (ECF), developed to support professional learning across both higher education (HE) and vocational education (VE) contexts. The ECF maps educator capabilities across four developmental levels and four domains of practice, aligning individual growth with institutional strategy and sector benchmarks. Guided by a stakeholder-informed methodology, twelve interviews with university leaders shaped the framework’s design, revealing key themes including inclusive teaching, reflective practice, curriculum coherence, and industry engagement. The ECF’s capabilities-based approach offers a replicable model for institutions seeking to build a skilled, adaptable academic workforce. This research contributes to the national conversation on educator development by demonstrating how co-designed frameworks can foster equity, responsiveness, and strategic alignment in professional learning across dual-sector institutions.

Generation Alpha, born entirely in the 21st century, is set to transform higher education through their distinct behavioural, epistemological, and technological traits. This paper critically examines how their expectation that are shaped

Generation Alpha, born entirely in the 21st century, is set to transform higher education through their distinct behavioural, epistemological, and technological traits. This paper critically examines how their expectation that are shaped by personalisation, digital immersion, and non-linear knowledge acquisition, challenge traditional undergraduate education models. Drawing on an informed critique literature review, the study explores the implications of Generation Alpha’s learning preferences and proposes a future-focused response through higher education learning design. The Adaptive Learning Ecosystem (ALE), developed at the University of Sydney Business School, is introduced as a model for reimagining curriculum, assessment, and student engagement. The model supports personalised, transdisciplinary, and socially connected learning, enabling students to navigate complex educational journeys with agency and purpose. The paper outlines three strategic horizons (program design, lifelong learning, and space spaces for innovation) through which higher education institutions can respond to the educational ambitions of Generation Alpha. We argue for a co-designed, human-centred approach to learning that prepares students not for today’s technologies and crises, but for the unknown and yet to be experienced challenges of tomorrow. In doing so, universities can remain relevant, resilient, and impactful in an era of continuous change.

This paper presents a case study of the development of two fully online undergraduate programs, which reframes course development from a complicated problem requiring linear solutions to a complex problem demanding collaborative, adaptive

This paper presents a case study of the development of two fully online undergraduate programs, which reframes course development from a complicated problem requiring linear solutions to a complex problem demanding collaborative, adaptive practices. As Australian universities face increasing demand for online education, traditional linear and transactional approaches fail to navigate the interconnected challenges of pedagogy, technology, quality expectations, and commercial realities. Drawing on design thinking and systems thinking, the authors propose a framework centred on six working principles: establishing clear scope through shared value and language, adopting team-focused collaboration, maintaining human-centred processes, embracing continuous iteration, utilising appropriate collaboration tools, and working across organisational layers. Practical innovations, including visual Course Maps, program workshops, a custom Smart Storyboard authoring tool, and a shared vocabulary of learning types and patterns, shifted focus from delivering minimum viable products to creating maximum value through meaningful collaboration and sustainable practices. The framework offers universities a replicable approach for developing high-quality online courses through design-led practices that acknowledge complexity while building institutional capacity and fostering educational innovation.

This study explores the ethical dilemmas faced by educators in managing student use of Generative AI (GenAI) in university assessments. Drawing on qualitative interviews with ICT educators at an Australian university, the research

This study explores the ethical dilemmas faced by educators in managing student use of Generative AI (GenAI) in university assessments. Drawing on qualitative interviews with ICT educators at an Australian university, the research identifies four key themes: threats to academic integrity, diminished skill development, emotional and ethical burdens on staff, and institutional gaps in policy and governance. Educators reported difficulty verifying authorship, concerns about AI-induced dependency, and frustrations with vague institutional guidelines. The study highlights the misalignment between academic restrictions and industry practices, raising questions about assessment authenticity and equitable access. In response, participants advocated for clearer policies, ethics education, and assessments requiring human judgment. The findings emphasise the need for systemic change, supported by Communities of Practice and university-specific AI tools aligned with educational values. While limited in scope, the study offers critical insights into how educators can uphold integrity and authenticity amid the increasing presence of GenAI in higher education.

As team teaching becomes increasingly common in higher education, understanding how teams of teachers coordinate their behaviours in the classroom is critical for effective instruction and instructional design. While prior research has

As team teaching becomes increasingly common in higher education, understanding how teams of teachers coordinate their behaviours in the classroom is critical for effective instruction and instructional design. While prior research has examined teaching behaviours through classroom observations, much of this work has tended to treat behaviours as isolated categories. This is, focusing on what occurs rather than how behaviours transition over time. Moreover, whether teachers with different levels of teaching experience exhibit distinct behavioural transitions in team teaching remains underexplored. This study addresses this gap by investigating how teaching behavioural transitions differ between high and low experience teachers working as a team in the classroom. Drawing on human-coded observations from 36 team-taught university sessions, and analysed using Transition Network Analysis (TNA), we visualised and compared patterns of behavioural transitions. The results revealed significant differences in behavioural transitions between teachers of varying experience levels. High experience teachers were more likely to transition directly from lecturing into interactions with students, and subsequently into real-time instructional adjustments, demonstrating instructional responsiveness and adaptability. In contrast, low experience teachers demonstrated a stronger reliance on peer coordination. This finding highlights the role of teaching experience in shaping team teaching dynamics and offers implications for teacher pairing and professional development.

Universities are using Artificial Intelligence as an add-on and not achieving its transformational benefits. In this presentation to Ascilite, a different kind of university is described: the futuristic “Global Artificial Intelligence

Universities are using Artificial Intelligence as an add-on and not achieving its transformational benefits. In this presentation to Ascilite, a different kind of university is described: the futuristic “Global Artificial Intelligence University (GAIU)” that applies AI to all its processes, as machine learning and artificial intelligence in education are finally coming of age – instead of an indifferent machine, AI has become a dedicated companion. The current capabilities of AI are projected into the future, while surprisingly new beneficial applications of AI could occur and transform GAIU even further. As with any educational technology, the potential and application of AI need to be guided by ethical, value-based considerations aimed at benefiting the students. While the GAIU Council initially believed that only the students would be real people, it became clear that a small group of other humans are also needed. Furthermore, a fundamental principle at GAIU remains that students want to interact with other people some of the time and this basic human need of “high touch” is balanced with the “ colder” AI algorithms. Perhaps this debate, along with the ever-more-responsively-evolving GAIU itself, signal a dramatic reassessment of what a digital-age university should be, and how humans and machines can excel together.

Constructive alignment requires that learning outcomes, teaching activities, and assessments be coherently structured. However, verifying this alignment, especially across large curricula, remains difficult at scale. In this paper, we

Constructive alignment requires that learning outcomes, teaching activities, and assessments be coherently structured. However, verifying this alignment, especially across large curricula, remains difficult at scale. In this paper, we present a Natural Language Processing (NLP)-based approach to automatically assess the alignment of Intended Learning Outcomes (ILOs), typically comparing smaller course-level objectives to broader Graduate Learning Outcomes (GLOs). Based on expert annotations, we fine-tune an NLP classifier to predict alignment with graduate learning outcomes focused on communication skills. Our results show that NLP tools can support alignment review by surfacing ambiguous phrasing and prompting expert judgement, offering a scalable and pedagogically grounded approach to curriculum quality assurance.

Artificial intelligence is rapidly transforming and reshaping higher education, assessment and learning design. Learning Designers working in higher education are no strangers to change; however, artificial intelligence is changing

Artificial intelligence is rapidly transforming and reshaping higher education, assessment and learning design. Learning Designers working in higher education are no strangers to change; however, artificial intelligence is changing assessment, education and course delivery faster than any previous change. In this era of rapidly evolving priorities, how do learning designers navigate this while applying and developing their personal values, balancing ethical considerations and improving their future capabilities. This study investigates the personal values and ethical considerations of learning designers and third space workers using artificial intelligence, the support received from their institutions to adapt and grow their skills in artificial intelligence use and considers how learning designers can build future learning design capabilities.

A growing number of third space practitioners work in academic developer (AD), educational technologist (ET) and learning designer (LD) roles in higher education to help academics navigate increasingly complex teaching practices. These

A growing number of third space practitioners work in academic developer (AD), educational technologist (ET) and learning designer (LD) roles in higher education to help academics navigate increasingly complex teaching practices. These specialist roles are referred to here as Educator Advisors (EdAdvisors). Inconsistent use of terminology and inaccurate role definitions for EdAdvisors can hinder the formation of sustainable collaborative relationships between EdAdvisors and academics by reducing understanding of their knowledge and skills. This mixed-methods study is comprised of a survey of EdAdvisors in 41 Australian higher education institutions and semi-structured interviews. It advances understanding of EdAdvisor roles by proposing descriptions which accommodate contextual differences within roles. Informed by a practice theory lens, it uses a combination of purposes, “practice bundles”, and capability types to support a new framework for describing EdAdvisor roles that addresses existing issues with role clarity which have largely not been resolved by more competency-oriented approaches. These descriptions for the three EdAdvisor roles are intended to stimulate discussion about practical steps to build understanding of people working in these roles in order to facilitate more effective collaborative working relationships between EdAdvisors and academics as they work to co-create innovative educational experiences.

The adoption of Generative Artificial Intelligence (GenAI) in Higher Education offers opportunities for innovation but also presents challenges for teaching and learning (T&L). Understanding its impact requires perspectives from discipline

The adoption of Generative Artificial Intelligence (GenAI) in Higher Education offers opportunities for innovation but also presents challenges for teaching and learning (T&L). Understanding its impact requires perspectives from discipline academic staff, academic integrity officers (AIOs), students, and industry partners. This study uses data from a two-year T&L project at the University of South Australia (UniSA), including surveys of 1,000 students, 80 discipline academic staff, and 19 AIOs, plus industry advisors, to capture perceptions two years after GenAI's widespread yet gradual adaptation. Academic staff across disciplines remain uncertain about GenAI’s effects on student work, raising concerns about critical thinking, engagement, feedback, and the attainment gap. Students’ experiences of GenAI vary across academic units: STEM and Education Futures students see benefits in generating insights and organising ideas. In contrast, Clinical & Health Sciences and Business students are more cautious. Non-native English speakers report higher perceived benefits from using GenAI. To safeguard Academic Integrity, AIOs recommend prioritising assessment redesign (88.2%) and ethical discussions with students (76.5%) over detection tools (39.5%). Markedly, while 90% of AIOs reported investigating GenAI-related misconduct, only 21% felt confident in their ability to detect it. Industry partners indicate their practices are evolving, with growing emphasis on skills like critical thinking (22%), problem-solving (18%), and adaptability (12%), emphasising the need for real-world assessments that foster these skills in academic settings. These findings highlight the need for a comprehensive institutional approach that includes assessment redesign, clear guidance, staff training, and ethical discussions to ensure responsible GenAI use while helping students develop essential skills. Strengthening the partnership between academia and industry can better align curricula with evolving employment requirements and prepare graduates for the changing workplace landscape.

In this paper, we reflect as third space professionals, two academic staff and two professional staff, working in higher education across Australia and Aotearoa New Zealand. Using collaborative autoethnography, we draw on Marty Neumeier’s

In this paper, we reflect as third space professionals, two academic staff and two professional staff, working in higher education across Australia and Aotearoa New Zealand. Using collaborative autoethnography, we draw on Marty Neumeier’s concept of “applied imagination” to explore how we envision the future of our work. Our dreaming is grounded in lived experience and shaped by the complex psychosocial landscape of navigating contemporary institutions. Through collective reflection, we interrogate not only our own identities but also the wider possibilities and tensions inherent in third space roles. Thematic analysis of our narratives revealed three central themes: agency, autonomy, and authority. We share a desire to be recognised as initiators and leaders who are trusted to shape the future of teaching and learning, not merely to implement the visions of others. For us, autonomy means intellectual and creative freedom; authority is about earned legitimacy and influence rather than formal power. While we acknowledge the tensions and dissonance that arise within this psychosocial landscape, we also find it a generative space for curiosity, collective sense-making, and new possibilities for third space professionals in universities.

This paper presents a collaborative initiative involving educators (online and in-class), disciplinary researchers, digital media and learning designers, and an industry-based specialist to co-design a digital storytelling approach for a

This paper presents a collaborative initiative involving educators (online and in-class), disciplinary researchers, digital media and learning designers, and an industry-based specialist to co-design a digital storytelling approach for a core first-year marketing course across five programs. Professionally relevant, industry-aligned curriculum is now a standard expectation among students, practitioners, and academics. However, first-year courses are typically theory-heavy, making it difficult for students to connect abstract concepts to real-world practice. To address this, we used a digital storytelling pedagogy to teach marketing students through work-simulated scenarios that place students at the centre of a fictional marketing team. The narrative structure was embedded across fully online and hybrid formats using a range of digital resources. This design enables students to engage with realistic professional challenges, apply theoretical knowledge, and build foundational problem-solving skills. A within-subjects research design was used to evaluate the impact of storytelling on student learning outcomes. Findings show that this approach creates more meaningful and engaging learning experiences, enhances the practical relevance of early tertiary education, and demonstrates the potential of digital storytelling as a bridge between theory and professional readiness. This work contributes to pedagogical innovation in marketing education and highlights the value of cross-functional collaboration in curriculum design.

Online discussion forums serve as a natural repository of students’ emotions and feelings in online learning environments, as students often post to seek assistance with academic challenges and difficulties. Understanding the complexity of

Online discussion forums serve as a natural repository of students’ emotions and feelings in online learning environments, as students often post to seek assistance with academic challenges and difficulties. Understanding the complexity of student emotions or affective states is crucial for fostering meaningful engagement and promoting academic success. This study focuses on confusion, a cognitive-affective state that emerges from complex learning processes and is recognised as potentially beneficial for deep learning when reaching an optimal state in terms of its duration and intensity. A text-based learning analytics (TLA) workflow grounded on the confusion and affect dynamics model was proposed. Six machine learning models were evaluated for their effectiveness in classifying confusion from online discussion forum posts collected from three courses. Results indicated that model performance was inconsistent. To demonstrate the viability of the proposed TLA workflow, it was applied to a comprehensive set of course data gathered from two study periods. Through systematic evaluation of students’ confusion states and transitions and their relationship with students’ academic performance, the study establishes the feasibility of the proposed workflow in facilitating educators’ early identification of students experiencing prolonged cognitive disequilibrium that may lead to unproductive struggle rather than meaningful learning gains.

This paper presents the results of a study to investigate the application of personalised assessment in supporting students’ learning of introductory programming. The participants of the study were university students majoring in

This paper presents the results of a study to investigate the application of personalised assessment in supporting students’ learning of introductory programming. The participants of the study were university students majoring in information technology and data analytics. The students in the study were added to a Moodle course called Computer Programming in Python, where they were allowed to take a test implemented using personalised assessment and another test implemented using traditional assessment (i.e., timed, fixed length and randomised ordering) as many times as they intend. The tests consist of mixed types of questions ranging from single word answers, True-False to multiple choice questions with three, four or five possible answers with one correct answer. The questions for the test using personalised assessment are divided into three categories according to the level of difficulty (easy, medium and hard). Based on these three categories, students’ abilities were estimated as Beginner, Intermediate, Advanced and Expert and detailed personalised feedback were provided instantly highlighting the concepts and issues in their attempts and linking with the course contents and course objectives. The results of the study show that the personalised assessment significantly outperforms traditional assessment with respect to usability, effectiveness and immediate quality feedback.

Comprising a significant portion of the Australian population (35.5% according to (Australian Bureau of Statistics, 2024), migrants often navigate complex socio-professional landscapes. This autoethnographic reflection explores the

Comprising a significant portion of the Australian population (35.5% according to (Australian Bureau of Statistics, 2024), migrants often navigate complex socio-professional landscapes. This autoethnographic reflection explores the transformative journey of an educator who migrated from India two decades ago as a skilled professional. Utilising Activity Theory (AT) as a conceptual framework, this paper delineates the major milestones, challenges, and instrumental mediations that have shaped the author's trajectory in the Australian higher education sector. By foregrounding the interplay of personal agency, systemic structures, and cultural dynamics, this account provides concrete examples of how initial hurdles were leveraged as catalysts for growth and the development of a nuanced professional identity. The paper draws upon retrospective journal entries and contemporary reflections to illuminate the multifaceted nature of migration beyond purely economic considerations, culminating in insights pertinent to future migrant educators and a connection to emerging research on systemic biases.

As Artificial Intelligence (AI) continues to evolve and influence higher education, ensuring that Assurance of Learning (AoL) frameworks remain both robust and adaptable, is vital. This paper presents a qualitative document analysis of

As Artificial Intelligence (AI) continues to evolve and influence higher education, ensuring that Assurance of Learning (AoL) frameworks remain both robust and adaptable, is vital. This paper presents a qualitative document analysis of Australian university AoL frameworks, examining their readiness for AI integration. The findings reveal a sector in early adaptation, marked by a tension between foundational AoL principles and AI's disruptive pace. We observe strategic AI acknowledgement often confined to policy, a nascent reform of graduate attributes to encompass AI literacy, and innovative practices largely isolated at the course or program level. Consequently, core AoL components such as curriculum mapping and assessment design, often remain disconnected from AI-assisted learning, limiting their reliability. This highlights significant conceptual and operational gaps, including varied institutional maturity, fragmented approaches, and a lack of sector-wide consensus or explicit guidance on ethical, observable AI-enhanced assessment. This paper calls for a shift from static quality assurance, defined by periodic compliance checks, to dynamic and systems-aware learning assurance. It proposes a future-focused AoL approach that embeds AI literacy, inclusive assessment design and stronger sector collaboration. As the first in a series, it contributes to ASCILITE 2025 by exploring how human expertise and AI can uphold academic standards.

In the digital age, where financial technologies (FinTech) are transforming personal finance behavior, equipping undergraduate economics students with Personal Financial Literacy (PFL) is essential. This study investigates the integration

In the digital age, where financial technologies (FinTech) are transforming personal finance behavior, equipping undergraduate economics students with Personal Financial Literacy (PFL) is essential. This study investigates the integration of Personal Finance Education (PFE) in economics curricula across Vietnamese universities and evaluates its impact on students’ financial capability. A mixed-methods approach was employed: content analysis of 120 course syllabi from six universities, combined with a quantitative survey of 400 third- and fourth-year economics students. Findings show that only 9.6% of programs include mandatory PFE courses. Regression analysis reveals that students who completed a PFE course scored significantly higher in PFL (Beta = 0.40, p < 0.001) even after controlling for income, gender, academic performance, and digital financial behavior. Notably, FinTech use not only has a direct positive effect on PFL (Beta = 0.35) but also amplifies the effectiveness of PFE through a significant interaction effect (Beta = 0.22). This suggests that digital engagement enhances financial education outcomes. The paper calls for a systemic shift toward integrating compulsory PFE in economics programs, aligned with FinTech practices and active learning strategies. Such an approach supports the development of financially capable, digitally fluent graduates, prepared for the complexities of the modern financial landscape.

Assessment classification plays a critical role in ensuring transparency, coherence, and academic integrity across higher education curricula. The rapid emergence of generative artificial intelligence (genAI) has intensified the need for

Assessment classification plays a critical role in ensuring transparency, coherence, and academic integrity across higher education curricula. The rapid emergence of generative artificial intelligence (genAI) has intensified the need for institutions to assure learning outcomes that are demonstrably independent of AI-generated content. This challenge is compounded by the need to manage institutional risks, particularly misconduct arising from inappropriate genAI use, and to uphold assessment validity as a measure of student learning. Despite this urgency, there remains a lack of structured, scalable frameworks that support both pedagogical intent and institutional governance in assessment classification. This paper addresses that gap by proposing a revised classification system, comprising assessment categories and types, designed to meet the diverse needs of students, educators, and governance bodies. The model aims to enhance clarity, support constructive alignment, and enable data-informed decision-making across the curriculum. Drawing on TEQSA’s genAI principles and the Australian Qualifications Framework (AQF), the approach integrates verb-based classification aligned with learning outcomes to support a course-level approach to assessment. While implementation is ongoing, the proposed model offers a practical, standards-aligned response to institutional and regulatory challenges, contributing a framework that supports a pedagogically sound assessment while strengthening institutional capacity to assess and mitigate risk.

Southern Cross University (SCU) has adopted a proactive, educative, and iterative approach to integrating Generative Artificial Intelligence (GenAI) into learning and assessment. Recognising the limitations of early binary models, SCU

Southern Cross University (SCU) has adopted a proactive, educative, and iterative approach to integrating Generative Artificial Intelligence (GenAI) into learning and assessment. Recognising the limitations of early binary models, SCU developed and trialled increasingly nuanced frameworks, culminating in a practical and pedagogically aligned three-level model. This model categorises assessments based on permitted GenAI use and is embedded into formal assessment infrastructure, offering clarity for students and staff while promoting ethical and context-sensitive engagement. Supporting materials, including descriptor guidelines and task-specific use cases, were co-developed with educators and students and informed by empirical research on student responses to GenAI use. This paper outlines the policy journey, highlighting how SCU’s model balances institutional integrity, academic innovation, and sector guidance.

Curriculum mapping is a critical component of accreditation and continuous improvement in education. However, aligning Course Learning Outcomes (CLOs) to professional standards such as Engineers Australia’s Stage 1 Competency Standard

Curriculum mapping is a critical component of accreditation and continuous improvement in education. However, aligning Course Learning Outcomes (CLOs) to professional standards such as Engineers Australia’s Stage 1 Competency Standard remains time-consuming and labour-intensive. This study evaluates the potential of generative AI (GenAI) to support curriculum mapping by comparing automated outputs with expert human judgement. A stratified sample of 141 (10%) first-year CLOs from a national dataset was analysed using both manual review and GenAI (ChatGPT 4o). Each CLO was mapped to the 16 EA Stage 1 Competencies, and outcomes were classified as Match, Manual Only, GenAI Only, or Neither. Overall agreement was 81.2% with particularly strong alignment in professional and personal attributes. Mismatches were most common in technical competencies, where GenAI over-mapped based on keywords or under-mapped due to limited contextual understanding. These findings suggest GenAI can support curriculum review at scale but requires expert oversight for nuanced or discipline-specific outcomes. The work in progress study contributes to the growing literature on AI in education and offers practical insights into hybrid approaches for accreditation and curriculum design.

Simulation-based learning (SBL) offers students effective learning opportunities to practice and develop clinical skills but can be challenging to maintain long-term. For this study, we developed authentic video simulations requiring

Simulation-based learning (SBL) offers students effective learning opportunities to practice and develop clinical skills but can be challenging to maintain long-term. For this study, we developed authentic video simulations requiring clinical decision-making regarding appropriate antibiotic selection, to enrich the learning experience for students; and evaluated their impact on student learning and satisfaction. Two scenarios (tuberculosis and polymicrobial infection) were developed with expert input and filmed using professional actors and a small film crew. Between 2019 and 2024, second-year pharmacy students participated in SBL activities utilising the videoed scenarios. Evaluation was via pre- and post-tutorial questionnaires. Over a five-year period, pre- and post-activity questionnaires were completed by 233 students (62.5%) for tuberculosis and 275 (54.9%) for polymicrobial infection. Statistically significant differences between pre- and post-tutorial questionnaire scores were observed. Most students reported the SBL activities were outstanding/excellent (80.0-93.6%) and helped them to acquire critical thinking skills (mean: 90.1% tuberculosis; 93.2% polymicrobial infection). Positive outcomes were consistent across the five-year timeframe. SBL activities involving video simulations were a sustainable approach to enhancing students’ learning experience, and supported consolidation of knowledge about antimicrobial agents and practice of clinical decision-making skills in selecting appropriate antibiotics to treat infectious diseases.

This paper presents a new take on constructive alignment that follows an active, conversationalist approach to curriculum design. It introduces a practical tool that can be used iteratively to achieve constructive alignment. Ensuring

This paper presents a new take on constructive alignment that follows an active, conversationalist approach to curriculum design. It introduces a practical tool that can be used iteratively to achieve constructive alignment. Ensuring constructive alignment is an important yet complex task, which often educators find challenging to master due to time and the complexities involved. The simplicity of the proposed tool addresses this and has considerable potential to be applied at scale across the higher education sector to develop new curriculum and to facilitate continuous improvement of existing curricula. By repositioning the educator to be an integral part of constructive alignment, it recognises that while constructing alignment, the educator is in fact constructing a deeper understanding of what the learning narrative can be. Understanding the synergies of various elements interacting in a course will not only empower the educator to develop meaningful learning experiences but also allow them to help learners understand similar connections. It also presents a tremendous opportunity for technology companies to streamline their grading tools so that the attainment of individual outcomes necessary to satisfy constructive alignment principles can be explicitly and automatically captured.

The Project-work Artificial Intelligence Integration Framework (PAIIF) was developed to guide educators in embedding AI tools into project-based learning in engineering education. Grounded in the CDIO model, PAIIF extends the traditional

The Project-work Artificial Intelligence Integration Framework (PAIIF) was developed to guide educators in embedding AI tools into project-based learning in engineering education. Grounded in the CDIO model, PAIIF extends the traditional four project stages (Conceive, Design, Implement, Operate) by incorporating four additional sub-stages that emphasise evaluation, communication, and reflection. This paper presents the early application of the PAIIF across six undergraduate and one postgraduate engineering course at five Australian universities during 2024. To evaluate the framework’s effectiveness, a two-phase survey was deployed to capture students’ understanding and usage of GenAI tools before and after the integration. The implementation was tailored to each institution’s policies and curricular context, with instructors selecting relevant stages of the framework based on course learning objectives. This paper offers a snapshot of how GenAI tools were introduced to support ideation, documentation, and evaluation tasks within project work. While full survey results are still being analysed, early observations suggest that PAIIF provided a valuable scaffold for ethical and pedagogically aligned GenAI use. This work contributes initial practical insights for educators seeking to apply structured AI integration in engineering education and lays the foundation for future large-scale evaluations.

In the era of generative AI, prompting has become a ubiquitous, often invisible pedagogical act. As educators and students increasingly rely on gen-AI systems, prompting functions as a primary interface through which knowledge is sought

In the era of generative AI, prompting has become a ubiquitous, often invisible pedagogical act. As educators and students increasingly rely on gen-AI systems, prompting functions as a primary interface through which knowledge is sought, structured, and legitimised. This shift reconfigures pedagogical authority, agency, and epistemic legitimacy often in ways that reinforce epistemic injustice. Grounded in critical and postcolonial theories, the paper examines how prompting mediates access to knowledge and reinforces existing systemic inequalities. We argue that pedagogical interactions with gen-AI become a form of epistemic gatekeeping, privileging certain forms of language, logic, cultural and ontological norms. We articulate our position that the wholesale adoption of gen-AI demands a critical reckoning with its epistemic consequences, particularly the ways it automates and obscures inherited hegemonic and colonial knowledges. We call for a decolonial approach to gen-AI in curricula, beginning with the relational work of deep listening to the scholars and communities who have long imagined otherwise, and a commitment to act on what they have already asked for. We challenge higher education to resist the seductive efficiencies of gen-AI and confront the epistemological stakes of its choices.

The growing shift to digital education environments has accelerated the need for inclusive, flexible, and pedagogically sound learning strategies. Authentic e-learning, when paired with virtual site visits (VSVs), offers learners

The growing shift to digital education environments has accelerated the need for inclusive, flexible, and pedagogically sound learning strategies. Authentic e-learning, when paired with virtual site visits (VSVs), offers learners opportunities to engage with complex, real-world contexts in ways that are both accessible and equitable. This paper explores how VSVs can support authentic learning goals by eliminating traditional barriers associated with physical site visits, using the example of a detailed virtual simulation of the mechanical plant room in the Alan Gilbert Building at the University of Melbourne. Drawing from constructivist, experiential, and situated learning theories, this case study illustrates how virtual environments can foster deep learning and skill development. It also outlines inclusive design practices, challenges in implementation, and strategies for promoting equity. Through a case study and practical design experience, the paper proposes that inclusive VSVs are not just a temporary substitute, but a lasting innovation for 21st century education.

Preparing future engineers requires moving beyond traditional, siloed teaching methods toward approaches that build adaptability, resilience, interdisciplinary collaboration, and development of a robust engineering identity. However, many

Preparing future engineers requires moving beyond traditional, siloed teaching methods toward approaches that build adaptability, resilience, interdisciplinary collaboration, and development of a robust engineering identity. However, many first-year engineering courses still emphasise theoretical content over practical, real-world skills. In response, this paper presents a food delivery robotics project co-developed by the School of Electrical Engineering and Telecommunications and UNSW Makerspace as a case study in curriculum design and innovation. The project uses hands-on learning, iterative skill-building, and a modular arena to engage students in authentic engineering challenges. By embedding experimentation, failure, and teamwork into the learning process, the initiative fosters a growth mindset and mirrors the collaborative nature of the engineering profession. This paper outlines the challenges and solutions involved in integrating Makerspace practices into the curriculum and highlights how such approaches can better prepare students for the evolving demands of the profession. The success of this initiative underscores the potential of interdisciplinary, practice-based education in cultivating the next generation of agile, industry-ready engineers.

This paper presents RMIT University's Digital Learning Environments Innovation Engine (DLEIE) a strategic, experiment-driven framework for continuous and scalable innovation in digital learning. Conceived as a post-COVID initiative to

This paper presents RMIT University's Digital Learning Environments Innovation Engine (DLEIE) a strategic, experiment-driven framework for continuous and scalable innovation in digital learning. Conceived as a post-COVID initiative to create a vibrant Digital Campus, the DLEIE evolved from RMIT's earlier Digital Campus conceptualisation. It addresses systemic challenges in digital learning experiences faced by large, multi-campus institutions with diverse learners, siloed governance, and complex technological ecosystems which affect these organisations to respond in agility. The DLEIE emphasises low-risk experimentation, institutional ownership, and rapid, user-informed iteration cycles. In 2024, five targeted experiments addressed student engagement, wellbeing, personalisation, and usability within Canvas LMS. These interventions including academic calendar overlays, wellbeing nudges, course navigation redesign, digital identity tools, and micro-feedback mechanisms tackled longstanding gaps in digital learning design. The 2025 strategy expands through cross-disciplinary focus groups, qualitative interviews, and co-design hackathons involving students, staff, and industry partners. We examine early outcomes, lessons learned, and cultural and structural enablers necessary to embed agile innovation in higher education. The DLEIE offers a replicable model for universities seeking to move beyond vendor-led digital change toward more responsive, inclusive, and effective digital learning environments.

This concise paper reports on a work-in-progress research project that involves the design, development and testing of learning analytics dashboards (LADs) designed to actively promote students’ and educators’ agency. A set of design

This concise paper reports on a work-in-progress research project that involves the design, development and testing of learning analytics dashboards (LADs) designed to actively promote students’ and educators’ agency. A set of design principles were developed through analysis of literature on student-facing LADs that related to principles of inclusivity, transparency, explainability and agency. This set of principles informed the design and development of a prototype simulated LAD used for research purposes with university students from equity and non-equity groups. This paper explains and illustrates how these design principles were translated into design features that empower students to take control of their learning and build their data literacy. This work addresses a key shortcoming of most current dashboards, highlighting the need to develop LAD designs that address the nexus between learner inclusivity, equity and agency.

Developing and maintaining student relationships are crucial in tertiary education because they impact engagement, retention, and learning. The Student Relationship and Engagement System (SRES), developed at the University of Sydney, is a

Developing and maintaining student relationships are crucial in tertiary education because they impact engagement, retention, and learning. The Student Relationship and Engagement System (SRES), developed at the University of Sydney, is a tool that integrates within a legacy learning management system and enables customised communications with students based on criteria such as attendance, assessment performance, and login frequency. This study utilised interviews from N = 25 SRES users across Curtin University faculties to explore its effectiveness and challenges, as well as what features could be added to SRES to improve its usefulness. Findings revealed that the initial usage focused routine and efficiency-driven tasks. However, advanced users leveraged the tool for more complex and adaptive uses, particularly in clinical or practical settings. While less experienced users found SRES beneficial primarily in large classes, more experienced users valued it irrespective of class size when linked to assessments and learning. Future enhancements suggested by participants included integrating artificial intelligence for generating communications or criteria automation, indicating potential advancements for SRES's utility.

Generative AI (GenAI) offers transformative potential in education, yet the real-world adoption of (Gen)AI in education remains slow and uneven. This study aims to validate a new instrument measuring educators’ perceived cognitive and

Generative AI (GenAI) offers transformative potential in education, yet the real-world adoption of (Gen)AI in education remains slow and uneven. This study aims to validate a new instrument measuring educators’ perceived cognitive and socio-affective trust in GenAI as a collaborator and to investigate if this trust varies across teaching levels, experience and academic qualifications. Inferential analyses such as One-Way ANOVA, nested ANOVA and General Linear Modelling (GLM) were conducted on self-reported data from 212 educators in Singapore. The findings revealed that educators with doctoral degrees in professional/adult education demonstrated the highest cognitive and socio-affective trust, while educators at the primary education level reported the lowest. These findings challenge previous research that reported no significant demographic or professional differences in trust in AI, highlighting the need for designing GenAI and its adoption strategies that are tailored to educators’ professional context and training. By identifying educators’ trust as a key factor in GenAI adoption, this study advances a human-centred perspective on trust in GenAI, particularly within the context of human-AI collaboration in teaching practice.

Australian universities face a critical disconnect between the predominance of monolingual instruction and the multilingual realities of the student population at the tertiary level. With one-fifth of the Australian population speaking a

Australian universities face a critical disconnect between the predominance of monolingual instruction and the multilingual realities of the student population at the tertiary level. With one-fifth of the Australian population speaking a language other than English, existing educational paradigms often fail to foster inclusive learning environments that reflect this linguistic diversity present in the student population. The gap necessitates a fundamental shift in language instruction, which must challenge monolingual conventions and accommodate students from more diverse linguistic backgrounds. While generic translation tools are widely available, they frequently produce fragmented or inaccurate outputs, especially when applied to complex academic content. In the absence of a clear national language policy, this concise paper argues that fine-tuned artificial neural networks used in machine translation, when trained on annotated parallel data, can bridge the equity language instruction gap and facilitate the implementation of multilingual instruction as part of a transformative shift led by educators to ensure no student is left behind.

This paper presents the creation of 'implementables': ready-to-implement teaching resources designed to nudge educators towards more inclusive approaches. It addresses the challenge of translating EDI theory into practice. Drawing on

This paper presents the creation of 'implementables': ready-to-implement teaching resources designed to nudge educators towards more inclusive approaches. It addresses the challenge of translating EDI theory into practice. Drawing on frameworks such as CDIO (Conceive-Design-Implement-Operate) and Advance HE’s EDI curriculum standards, our group mapped connections between effective learning design and inclusive pedagogical practices. This work highlights the significant overlap between principles of good pedagogy and inclusive teaching practice, and resulted in a resource, Equity in Action, consolidating practical, actionable examples and stretch goals for educators. The work underscores the importance of practical support for educators at varying levels of EDI expertise and advocates for ongoing, community-driven efforts to foster sustainable, meaningful change in higher education environments.

In an era of rapid institutional and digital transformation, navigating leadership in higher education’s third space presents complex identity challenges, particularly for women. Through an interactive autoethnographic process, five

In an era of rapid institutional and digital transformation, navigating leadership in higher education’s third space presents complex identity challenges, particularly for women. Through an interactive autoethnographic process, five participants in the ASCILITE Community Mentoring Program for Women in Leadership (CMP-WiPL) explored these challenges over 18 months, combining personal reflection, peer dialogue, and professional identity practice. Through iterative cycles of self-assessment, professional identity building on LinkedIn, and collaborative discussions within our WiPL Community of Practice (CoP), the group created and refined the Leadership Identity Framework for Transformation (LIFT). Informed by theoretical models of professional identity, third space capabilities, and Communities of Practice, LIFT offers a practical and adaptable approach to scaffold identity development, address imposter syndrome, and fosters inclusive leadership. Findings highlight the power of collective sense-making, digital tools, and structured reflection in supporting professional growth. By framing leadership as a continuous reflective journey, this work provides a sustainable model for enabling confident, authentic leadership in higher education and the third space sectors shaped by uncertainty and change.

This paper tells the story of how I came to use constructivist grounded theory (CGT) as a method to facilitate my own reflection. As an online teacher, the speed at which things happen, the constant turnover and sheer number of decisions

This paper tells the story of how I came to use constructivist grounded theory (CGT) as a method to facilitate my own reflection. As an online teacher, the speed at which things happen, the constant turnover and sheer number of decisions made across courses meant that most of my decisions were the result of reflection-in-action rather than reflection-on-action. Whilst existing reflective models provided some insight, for me what they were lacking was a robust, established, research-oriented framework, and an overall outcome I could conceptualise, share and communicate. After telling the story of how I came to use CGT, I provide a brief overview of the various process in CGT, a brief rationale for how these link to, or enable reflection, how they were used, and my thoughts on the overall process. The paper concludes with some final thoughts and actions for moving forward.

This study investigated the impact of chatbot and AR-based feedback scaffolding on university students’ cognitive load during a scientific simulation inquiry. In a randomized experiment, 118 university students received feedback from one

This study investigated the impact of chatbot and AR-based feedback scaffolding on university students’ cognitive load during a scientific simulation inquiry. In a randomized experiment, 118 university students received feedback from one of four scaffolding conditions: traditional, chatbot, augmented reality (AR), or a combined chatbot-AR system. Cognitive load was assessed using a validated self-report scale, complemented with an open-ended questionnaire. Results indicated that chatbot scaffolding significantly increased intrinsic load, and the combined chatbot–AR scaffolding group showed significantly higher extraneous load than the AR-based group. These findings suggest that integrating multiple forms of scaffolding may elevate cognitive burden and should be implemented with caution to prevent excessive load on learners.

This paper reports on the Innovation Lab: Transform, a six-month professional development program in the Learning Design and Technology unit at the University of Technology Sydney. Designed during a period of rapid change (generative AI

This paper reports on the Innovation Lab: Transform, a six-month professional development program in the Learning Design and Technology unit at the University of Technology Sydney. Designed during a period of rapid change (generative AI, assessment security), the program created a structured, in-person space for experimentation through Creative Foundations workshops and a team-based Idea Accelerator. Co-created values and distributed facilitation helped establish psychological safety, while feedback showed gains in creative confidence, collaboration, and willingness to test bold ideas. The success of the Transform program has since anchored a broader innovation agenda framed around three streams: Optimise (incremental improvements), Extend (modular innovation), and Transform (capability-building for radical change). This paper demonstrates how a single localised initiative can evolve into a multi-layered strategy, balancing day-to-day enhancement with capacity for systemic innovation. Insights are offered for third space professionals and leaders seeking to embed sustainable innovation in higher education.

This study investigates university teachers’ and learning designers’ perceptions of students’ cognitive engagement in online learning. Using qualitative data, five key behavioural indicators were identified: active participation

This study investigates university teachers’ and learning designers’ perceptions of students’ cognitive engagement in online learning. Using qualitative data, five key behavioural indicators were identified: active participation, application of knowledge, preparation, collaboration, and high-quality contributions. Teachers emphasised real-time engagement through digital tools and classroom interactions, while learning designers highlighted the role of instructional scaffolding and teacher preparedness in supporting deeper learning. Collaborative learning was a shared focus, with peer interaction seen as a driver of critical thinking and reflection. A notable divergence in emphasis emerged: teachers prioritised observable behaviours, whereas designers focused on pedagogical design. This suggests a need for better alignment between teaching practice and instructional design to enhance engagement. The findings highlight the interplay between student agency and structured learning environments, offering insights into optimising online education for sustained cognitive effort and meaningful learning.

In response to the challenges of academic integrity and the rise of generative AI in higher education, this study explores how Interactive Oral Assessment (IOA) can be used alongside traditional written assessments to better evaluate a

In response to the challenges of academic integrity and the rise of generative AI in higher education, this study explores how Interactive Oral Assessment (IOA) can be used alongside traditional written assessments to better evaluate a capstone action research project in graduate teacher education. The study was conducted in the Master of Teaching program at Universiti Brunei Darussalam, where student teachers complete a 12,000-word Research Exercise in their final semester. Conducted in three phases, the study involved two pilot stages to refine the assessment design and support structures, followed by full implementation. This study presents preliminary findings from Phase 3 (full implementation) and survey data from Phase 2 (formative IOA pilot). Across both phases, student teachers reported that the IOA process was engaging and authentic, providing enhanced opportunities for critical reflection and meaningful communication. Initial insights suggest that IOA not only supports student voice but also strengthens the validity of assessment through its alignment with real-world teaching competencies. While further analysis is ongoing, these early results highlight IOA’s potential as an authentic, responsive, and future-focused assessment strategy in teacher preparation.

The rapid rise of generative artificial intelligence (AI) tools has intensified universities’ long-standing challenge of ensuring that student work is authentic, an issue that is particularly acute in fully online programs. In 2024, new

The rapid rise of generative artificial intelligence (AI) tools has intensified universities’ long-standing challenge of ensuring that student work is authentic, an issue that is particularly acute in fully online programs. In 2024, new Tertiary Education Quality and Standards Agency (TEQSA) guidelines prompted our university to revise its Assessment for Learning Policy and Procedures, requiring every unit to have a secure assessment that is explicitly mapped to learning outcomes and validates core knowledge and skills. This study reports on the first pilot of that policy in three wholly online, postgraduate units. Using the 4-A Framework for secure assessment design, we implemented remotely-invigilated final exams in two units and a live, oral presentation in the third. Semi-structured interviews are planned with academics and students, and completion data and incident reports will be analysed. Preliminary pilot findings indicate that secure tasks can credibly validate student ownership of learning outcomes in an online environment. While both formats reduced academic-integrity breaches, oral presentations garnered higher perceptions of authenticity and lower than expected administrative overheads. We argue that secure, outcome-mapped assessments are feasible, scalable, and pedagogically valuable in online postgraduate education, and we outline policy and practice implications for institutions facing similar regulatory pressures.

Bridging the knowing–doing gap remains a formidable challenge in large foundational higher education courses. This study examines the integration of structured reflective learning journals, grounded in experiential learning theory, within

Bridging the knowing–doing gap remains a formidable challenge in large foundational higher education courses. This study examines the integration of structured reflective learning journals, grounded in experiential learning theory, within a first-year economics curriculum to foster metacognition, student engagement, and authentic knowledge transfer. Weekly guided prompts invited learners to connect course content with personal insights and real-world applications, while a peer review intervention was later introduced to scaffold evaluative judgement and deepen reflective practices. Across three semesters, consistently high participation and improved performance outcomes were observed, particularly with the inclusion of peer review, suggesting that reflective writing supported by peer review cultivates meaningful engagement and enhances critical thinking. This paper highlights reflective practice and peer review as a potential catalyst for meaning-making and learner growth in contemporary higher education.

As online adult education rapidly expands, driven by technological advancements and post-pandemic demands, there is a critical need to reimagine evaluation and learning frameworks that address the limitations of traditional methods. This

As online adult education rapidly expands, driven by technological advancements and post-pandemic demands, there is a critical need to reimagine evaluation and learning frameworks that address the limitations of traditional methods. This study proposes a novel Design-Based Research (DBR) framework to develop AI-enhanced learning environments tailored for solving complex, real-world problems. By integrating heutagogy, authentic learning, and transformative learning theory, the research aims to create a transferable model that empowers adult learners to navigate interdisciplinary challenges. Expected outcomes include a transferable design framework, ethical AI guidelines, and scalable principles for lifelong learning, addressing gaps in traditional evaluation methods while prioritizing learner-centric and context-sensitive innovation.

Programming skills are essential across STEM and data-intensive fields, yet many students, particularly women, experience anxiety and lack confidence in their coding abilities. To address these issues, we developed an extracurricular

Programming skills are essential across STEM and data-intensive fields, yet many students, particularly women, experience anxiety and lack confidence in their coding abilities. To address these issues, we developed an extracurricular coding challenge using Karel the Robot for undergraduate and postgraduate business students enrolled in introductory data science units. We conducted a mixed-methods study to evaluate the effectiveness of the intervention. Participants who engaged with the challenge reported an increase in positive sentiment towards programming and confidence. Notably, women exhibited larger gains in confidence than men, despite facing the stereotype about gendered ability. These findings indicate that playful and low-stakes environments can empower underrepresented learners.

This paper reflects on a co-design initiative with students with visual impairment to improve accessibility in a Canvas-based learning environment at an Australian-owned university in Vietnam. The project shifted practice from reactive

This paper reflects on a co-design initiative with students with visual impairment to improve accessibility in a Canvas-based learning environment at an Australian-owned university in Vietnam. The project shifted practice from reactive compliance with WCAG standards to a more relational, proactive approach—embedding user feedback during the design phase. Situated within a broader context of limited inclusion in Vietnamese higher education, the paper argues that technical standards alone cannot ensure meaningful accessibility. Co-design fostered practical improvements and prompted mindset shifts among learning designers. Cultural values such as trust and collaboration further supported the process. The paper concludes by identifying implications for institutions in similar contexts and future applications for supporting neurodivergent learners through co-design.

This paper introduces the preliminary development of the Teaching Balance Model (TBM), a conceptual framework to help educators navigate competing discourses in contemporary higher education. Developed through grounded theory methods

This paper introduces the preliminary development of the Teaching Balance Model (TBM), a conceptual framework to help educators navigate competing discourses in contemporary higher education. Developed through grounded theory methods combined with individual and collaborative reflection, the TBM responds to tensions heightened by increasingly complex teaching environments—particularly in technology-enhanced, online, and hybrid contexts. Rather than viewing challenges such as theory versus practice, learning versus assessment, or students as partners versus customers as binary oppositions, the TBM encourages an integrative approach. Data were generated through reflective memos and coding of teaching decisions and rationales from three business academics with experience across online, hybrid, and face-to-face settings. Through iterative coding, constant comparison, and theoretical sampling, key categories—such as value priorities and decision-making rationales—emerged to inform the model. The TBM proposes a balanced middle ground that supports inclusive, values-driven teaching while acknowledging systemic pressures. We discuss the model’s potential as a reflective tool for practitioners and its future development for research into academic identity, wellbeing, and institutional culture. The TBM aims to contribute to conversations about sustainable and equitable teaching practice in digitally mediated learning environments.

This paper presents a case study of a predictive analytics pilot aimed at improving student retention in postgraduate online learning. By leveraging Learning Management System (LMS) engagement metrics, students who had not accessed or

This paper presents a case study of a predictive analytics pilot aimed at improving student retention in postgraduate online learning. By leveraging Learning Management System (LMS) engagement metrics, students who had not accessed or submitted their first assessment at least five days before the due date, were flagged for early outreach. A tiered human-led interventions model via email, phone, and SMS were triggered to provide personalised support. The pilot was implemented across 12 postgraduate subjects in 2025 and benchmarked against 2024 cohorts. A 1.9 percentage point improvement in dropout rate was observed, equating to approximately 32 additional students retained. These findings highlight the practical value of low-complexity, data-informed interventions and the role of AI-human synergy in supporting online learners. This pilot demonstrates a scalable approach to integrating predictive analytics with personalised support in fostering adaptable learners and improving student outcomes, particularly in resource constrained settings.

While academic misconduct prevention has traditionally focused on student behaviour, limited attention has been paid to the role of educator judgment—particularly under the influence of implicit bias and AI-generated suspicion. As online

While academic misconduct prevention has traditionally focused on student behaviour, limited attention has been paid to the role of educator judgment—particularly under the influence of implicit bias and AI-generated suspicion. As online proctoring software becomes more prevalent, concerns arise regarding systemic disadvantages experienced by specific student groups, particularly female students, students of colour, and neurodiverse learners. This positioning paper explores how AI-driven proctoring technologies, combined with the cognitive demands on academics, may inadvertently amplify reliance on bias and heuristic judgment in academic misconduct decisions. Emerging evidence suggests that certain student groups are disproportionately flagged by proctoring systems and subjected to harsher scrutiny, raising concerns about procedural fairness and equity in online assessments. Rather than reporting empirical findings, this paper outlines a research agenda to investigate how identity-related cues influence both AI flagging and academic judgment. We propose a mixed-method approach—combining meta-analysis with vignette-based quasi-experiments—to critically examine the intersection of bias, surveillance, and academic integrity.

Generative AI (GenAI) is reshaping the practice of teaching and learning in higher education, creating new possibilities alongside significant complexity. While interest in AI tools continues to grow, meaningful adoption relies on

Generative AI (GenAI) is reshaping the practice of teaching and learning in higher education, creating new possibilities alongside significant complexity. While interest in AI tools continues to grow, meaningful adoption relies on approaches that are grounded in pedagogy, flexible, and responsive to context, rather than on technical proficiency alone. This paper reports on a project to embed AI capability development for educators, into existing professional learning programs, positioning AI as a tool to extend and enrich, rather than replace, innovative teaching practices. The project focuses on creating opportunities for educators to explore, test, and adapt AI tools within their specific teaching contexts, moving beyond generic training that often fails to address the complexities of practice. Design principles and implementation strategies prioritise equity, educator agency, and engagement, supporting sustainable and inclusive professional learning in online education. Early insights highlight the value of integrating AI capability into pedagogical initiatives rather than treating AI as a stand-alone technical skill. The outcomes of this work contribute a practical model for AI capability building that can inform future professional learning and institutional strategies for adaptable and innovative teaching in an era of continuous change.

Research on online quizzes has surged in recent years, focussing on providing pedagogical insights and success stories. While these studies indicate educational benefits of online quizzes, most of the empirical ones are focused on a single

Research on online quizzes has surged in recent years, focussing on providing pedagogical insights and success stories. While these studies indicate educational benefits of online quizzes, most of the empirical ones are focused on a single course as opposed to institution-wide studies. This study sought to investigate the pedagogy underpinning the use of online quizzes by academics by examining their authentic assessment statements. The analysis of over 2,500 words of academic statements demonstrated that quizzes are primarily designed to assess comprehension and application of knowledge, reflecting their strong pedagogical role in facilitating acquisition and practice types of learning.

The hidden curriculum includes unspoken and unwritten expectations that can impact student success, often reinforcing inequities in marginalised student groups. While often overlooked when enacting curriculum, addressing the implicit

The hidden curriculum includes unspoken and unwritten expectations that can impact student success, often reinforcing inequities in marginalised student groups. While often overlooked when enacting curriculum, addressing the implicit learning requirements of hidden curriculum is crucial for fostering inclusive and equitable academic experiences. Drawing on existing literature and case studies at Charles Darwin University, this paper presents a layered design model for Open Microlearning (OML) as a novel approach to explicitly support and scaffold hidden curriculum in higher education. OML combines Open Educational Practice with microlearning principles to provide quick, flexible, and bite-sized learning experiences using freely available, reusable resources and encourages collaboration with others. The layered model supports modular and accessible design, grounded in pedagogical intent and responsive to diverse learner needs. The layered structure allows for a clear separation of key components within the design, enabling focused attention to enhance overall coherence and effectiveness. The flexibility and adaptability of the OML layered model enables just-in-time learning to bridge hidden curriculum gaps and empower students to engage meaningfully with their learning and assessment.

Virtual Reality (VR) technology is transforming Vocational Education and Training (VET) by providing immersive and engaging learning experiences. However, its successful integration depends not only on technological adoption but also on

Virtual Reality (VR) technology is transforming Vocational Education and Training (VET) by providing immersive and engaging learning experiences. However, its successful integration depends not only on technological adoption but also on fostering resilience and adaptability among educators and learners in the face of rapid technological change. This proposed study will explore strategies for upskilling VET educators in VR, focusing on resilience, social-emotional well-being, and supportive professional development (PD). Using a mixed methods approach, this research will combine quantitative surveys and qualitative interviews to examine how VET educators can develop resilience and adaptability in VR-based training and assessments. The study will examine digital competency in Australia’s VET workforce by analysing survey data, mapping VR tools for trainers’ PD, and evaluating existing VR training opportunities. The study will contribute to strategies ensuring VET professionals equipped with the knowledge and skills needed to thrive in the digital age and the VET sector remains responsive to industry demands to prepare the learners to be part of a dynamic workforce.

The University of Western Australia aims to launch its first undergraduate nursing degree in 2026, co-designed from inception and underpinned by programmatic assessment for learning. This course was developed in direct response to

The University of Western Australia aims to launch its first undergraduate nursing degree in 2026, co-designed from inception and underpinned by programmatic assessment for learning. This course was developed in direct response to sector-wide disruptions, including generative artificial intelligence, workforce challenges, assessment reform, and evolving healthcare demands. Instead of adapting existing models, we created a curriculum intentionally designed to equip students to navigate and thrive in complex environments. This paper traces our design journey, from co-design with clinicians, educators, nursing alumni, and community partners to the creation of a cohesive, feedback-rich assessment system. Key features include low-stakes assessments, structured academic mentorship, course portfolios, case-based vivas, and holistic progression panels. These components empower students to integrate feedback, track their development, and build professional identity over time. We also address the policy tensions encountered, particularly the misalignment between course-level progression and traditional unit-based governance. Through adaptive moderation processes and strategic alignment with institutional frameworks, we established a robust model that balances innovation with compliance. This case study offers a scalable, future-focused approach to curriculum reform for professionally accredited programs, one that prioritises student growth, industry alignment, and sustainable practice in an era of ongoing change.

The emergence of generative AI has not broken assessment systems in higher education; it has exposed long-standing fractures. Overly complex tasks, excessive assessment demands and staff overload point to a system that has privileged

The emergence of generative AI has not broken assessment systems in higher education; it has exposed long-standing fractures. Overly complex tasks, excessive assessment demands and staff overload point to a system that has privileged compliance over human connection. This position paper acknowledges that brokenness but looks beyond it. Inspired by the Japanese art of Kintsugi, where cracks are repaired with gold, we ask: what if these fractures are openings for stronger, more relational practices to emerge? Adopting an autoethnographic lens, we draw on our experiences in curriculum design, academic leadership and learning support during a period of rapid change. We argue that trust is foundational in assessment design, enacted through dialogue, feedback, collaboration, and willingness to take risks. While the system may be under strain, the people are not broken. Designing with trust enables the reimagination of integrity. The future of assessment lies in transformation – built differently and built together.

This study explores the factors influencing students' intention to use generative AI (Gen AI) tools, such as ChatGPT, based on an extended Technology Acceptance Model (TAM) that incorporates ethics, trust, and social norms. Going beyond

This study explores the factors influencing students' intention to use generative AI (Gen AI) tools, such as ChatGPT, based on an extended Technology Acceptance Model (TAM) that incorporates ethics, trust, and social norms. Going beyond the traditional models that emphasise usefulness and ease of use, this study highlights the growing influence of ethical beliefs, peer influence, and trust in driving adoption behaviour. A survey of 316 students at an international university in Bangladesh revealed that intention is strongly driven by perceived usefulness and ease of use, and intention is a strong predictor of actual usage. However, trust and ethical concerns exert more subtle effects such that trust directly influences usage and moderates the intention-action link, while ethical concerns do not directly predict behaviour but strengthen the intention-to-use connection. Structural equation modelling confirmed multiple mediation paths, highlighting the complexity of GenAI adoption in an educational setting. These results suggest that adoption decisions are not simply technical but are shaped by students' trust in the tool, social signals from peers, and the accuracy and quality of responses. The study offers practical guidance for educators and policymakers seeking to promote ethical GenAI adoption in higher education institutions.

This paper describes the development and implementation of an innovative hotel carbon reduction simulation aimed at developing sustainability competencies in business students. Using the Harvard Business School "Net Zero" simulation, we

This paper describes the development and implementation of an innovative hotel carbon reduction simulation aimed at developing sustainability competencies in business students. Using the Harvard Business School "Net Zero" simulation, we assess how interactive simulation-based learning opportunities improve students' self-assessment of the eight sustainability competencies with a mixed-methods approach. The research examines critical gaps in knowledge of how simulation-based methods can develop integrated competency sets needed to solve complex sustainability challenges. The simulation assigns around 950 first-year management students as hotel managers tasked with achieving 50% emission reductions over seven years while maintaining financial performance. Students select from 29 sustainability initiatives across Energy, Purchasing, and Management categories, working within realistic constraints of carbon budgets, site-specific emission factors, and dynamic market conditions. Our comprehensive analytical design combines pre-post competency surveys with cluster analysis of strategic approaches, and qualitative analysis of learning reflections. Anticipated outcomes are enhanced competency development across all eight dimensions, with gains in systems-thinking, futures-thinking, and implementation competencies. The research aims to provide empirical proof for developing specific sustainability competences as well as demonstrating scalable approaches of integrating sustainability education into core business curriculum.

The literature has established that if instructors shy away from implementing Universal Design for Learning (UDL) it is often because of lucid and tangible fears about workload. In parallel, the emergence of gen AI has immediately

The literature has established that if instructors shy away from implementing Universal Design for Learning (UDL) it is often because of lucid and tangible fears about workload. In parallel, the emergence of gen AI has immediately triggered hopes that large language models (LLMs) might be successfully used to support the challenging planning tasks of higher education instructors; it has immediately struck scholars that this might include supporting instructors who might have fears about workload and competencies in relation to UDL implementation in their classes. This study explored the degree to which an LLM could be effective in supporting an instructor redesign two Masters of Education courses in order to make them more aligned with UDL than they already were. The theoretical lens used in this project was the social model of disability. The methodological framework used was action research. The research team prompted the LLM for UDL strategies. A process of triangulation invited students having previously taken the courses to assess the effectiveness of the redesign. The findings suggest that gen AI can indeed support the UDL redesign of courses. Concerns are, however, raised because mastering the prompting competencies necessary may be as complex as the UDL redesign itself.

This paper introduces a MPhil research project aimed at developing a framework to facilitate university educators, particularly non-technical users, in designing Generative AI (GenAI)-powered study support tools for regional, rural, and

This paper introduces a MPhil research project aimed at developing a framework to facilitate university educators, particularly non-technical users, in designing Generative AI (GenAI)-powered study support tools for regional, rural, and remote (RRR) students. The framework will guide the customisation of AI chatbots (AI-SUPBOT) that provide scaffolded academic support while upholding academic integrity. Grounded in Technological Pedagogical Content Knowledge (TPACK) and Design-Based Research (DBR) principles, the framework aims to offer a structured, pedagogically sound path for integrating GenAI in higher education.

This paper presents a scoping review of recent research on virtual exchange (VE) in higher education, with a focus on language learning and intercultural development. Drawing on 48 peer-reviewed studies published between 2020 and 2025 in

This paper presents a scoping review of recent research on virtual exchange (VE) in higher education, with a focus on language learning and intercultural development. Drawing on 48 peer-reviewed studies published between 2020 and 2025 in Q1 journals, the review analyses terminology, geographical distribution, linguistic and cultural goals, technologies used, research designs, data collection, and key findings. The results confirm that VE is a flexible and inclusive pedagogical model that fosters both language acquisition, particularly speaking skills, and intercultural competence, through tasks emphasizing collaboration and reflection. While commonly used tools include Zoom, WhatsApp, and Google platforms, few studies have integrated emerging technologies such as virtual reality (VR) and AI. Moreover, research in the Japanese higher education context remains limited. These gaps inform the design of a forthcoming empirical study that will connect Japanese university students with peers in Australia and New Zealand through VR- and AI-enhanced intercultural exchange. This review not only highlights best practices and methodological trends in VE research but also identifies underexplored areas and technological opportunities that can shape the next generation of intercultural language education.

This paper presents an innovative pedagogical approach designed to address critical challenges in Business Process Management (BPM) education, specifically supporting students in mastering complex Threshold Concepts (TCs). Employing First

This paper presents an innovative pedagogical approach designed to address critical challenges in Business Process Management (BPM) education, specifically supporting students in mastering complex Threshold Concepts (TCs). Employing First Principles of Instruction and structured around the BPM lifecycle, the course integrates authentic, staged industry case studies to bridge theoretical understanding with practical application. Active collaboration with industry partners, alongside innovative digital tools like process mining, enhances students’ analytical skills and professional competencies. This structured approach promotes transformational learning, demonstrating clear improvements in student engagement, comprehension, and preparedness for digital-era organisations. The framework's portability and scalability offer educators a robust, adaptable model that contributes meaningfully to ongoing discussions on curriculum innovation in BPM education.

This paper explores the integration of Artificial Intelligence technologies into pre-medical education through a case study of a half-day residential school activity and subsequent focus group discussion. The initiative aimed to enhance

This paper explores the integration of Artificial Intelligence technologies into pre-medical education through a case study of a half-day residential school activity and subsequent focus group discussion. The initiative aimed to enhance students’ awareness, confidence, and critical thinking regarding AI-Ts in clinical practice. Using a design thinking framework, students engaged in hands-on exploration of AI applications, scenario-based learning, and collaborative ideation. Focus group findings revealed increased awareness of AI capabilities and limitations, cautious optimism about its use in practice, and a desire for structured AI education. The study highlights the importance of embedding learning opportunities regarding the application of AI-Ts into medical curricula and offers recommendations for future educational strategies aligned with digital innovation in healthcare.

As a media developer embedded in a research-intensive university in Australia, I occupy a third space that spans academic, professional, and pedagogical domains. This autoethnographic study examines the persistent disconnect between

As a media developer embedded in a research-intensive university in Australia, I occupy a third space that spans academic, professional, and pedagogical domains. This autoethnographic study examines the persistent disconnect between academics’ content-heavy approaches to educational video production and students’ need for concise, engaging, and pedagogically aligned media. Drawing on my dual role as facilitator and observer, I reflect on the hidden labour involved in translating disciplinary content into video formats informed by principles such as Mayer’s Cognitive Theory of Multimedia Learning. Systemic and design-related barriers, including siloed workflows, limited academic training in multimedia pedagogy, and institutional imperatives for scalability, contribute to this misalignment. From my third space vantage point, I argue for reframing educational video production as a collaborative act of teaching. I propose the establishment of transdisciplinary design teams, the provision of pedagogical training for academics, the use of artificial intelligence to support script development, and the application of learner analytics to evaluate impact. This study contributes to ongoing discourse on multimedia learning by highlighting the role of third space professionals in advancing student-centred, theory-informed digital education in higher education contexts.

Diana Laurillard’s Conversational Framework has long provided a powerful model for designing technology-enhanced learning, emphasising dialogue, feedback, and the iterative exchange between conceptual understanding and practical

Diana Laurillard’s Conversational Framework has long provided a powerful model for designing technology-enhanced learning, emphasising dialogue, feedback, and the iterative exchange between conceptual understanding and practical application. Historically, building educational tools that supported these rich learning interactions required significant technical expertise and development resources. However, the emergence of Generative AI chatbots capable of generating, previewing, and sharing web application code, such as Anthropic Claude Artefacts as well as Open AI’s ChatGPT and Google Gemini’s Canvas functionality now allow educators and learning designers to describe an idea in natural language and receive working, interactive web applications in return. In this paper we explore how these tools enable the rapid creation of learning experiences that embody core principles of Laurillard’s Conversational framework. Drawing on real examples built using Anthropic Claude’s AI enabled Artefacts, we illustrate how AI-generated applications can support perspective-taking, problem-solving, feedback-driven learning, and structured reflection. From a Linear Regression explainer to a Multi-Perspective Elaboration Tool, these examples show how AI can now scaffold not just content delivery but interactive learning conversations. We argue that this shift lowers the barrier to educational innovation and empowers a broader community of practitioners to design learning tools. We conclude by discussing current limitations and outlining directions for future research and practice in AI-assisted learning design.

Writing a literature review is a challenging task for many students, particularly when it comes to locating relevant sources, synthesising findings, and citing accurately. This study presents the design and evaluation of a web-based tool

Writing a literature review is a challenging task for many students, particularly when it comes to locating relevant sources, synthesising findings, and citing accurately. This study presents the design and evaluation of a web-based tool that uses Retrieval-Augmented Generation (RAG) to support students in writing short literature reviews. The system allows students to upload academic papers, write drafts, and receive rubric-aligned feedback from a Large Language Model (LLM). A user study involving ten honours and postgraduate students examined how learners engaged with the tool during a two-hour session. User interaction logs, rubric-based scores, and survey responses were analysed to evaluate learning outcomes and user experience. All participants showed measurable improvements in their writing. Students reported that the tool helped improve their reviews, particularly in areas such as citation guidance, writing structure, and synthesis. They also suggested enhancements such as grammar checking, better interface design, concise feedback and improvement to the retrieval and citation validation. The findings suggest that LLM-powered feedback tools can effectively support academic writing when designed to encourage revision, reflection, and writing skill development.

In response to growing demand for flexible, industry-aligned education, learning designers are increasingly taking on strategic roles in complex, cross-sector curriculum initiatives. This paper presents a case study of the Macquarie

In response to growing demand for flexible, industry-aligned education, learning designers are increasingly taking on strategic roles in complex, cross-sector curriculum initiatives. This paper presents a case study of the Macquarie University (MQ), Faculty of Science and Engineering’s Learning Design Team who have played a pivotal role in the development of innovative, stackable micro-credentials for the Institute of Applied Technology – Digital (IAT-D), a unique partnership between MQ, TAFE NSW, University of Technology Sydney (UTS), and Microsoft. These micro-credentials were co-designed, co-developed, and co-delivered by academic, professional, and industry partners. Brought into the project midstream, the Learning Design Team faced compressed timelines, evolving expectations, and no established processes. In response, they adopted an agile, improvisational approach that epitomised the challenge of “paving the runway while landing the plane” simultaneously building systems and processes whilst delivering outcomes under extremely tight deadlines. Acting as instructional designers, developers, and project managers, the team consulted with project leadership, guided busy academics, and translated complex requirements into cohesive, student-centred learning experiences. This case highlights the often invisible but vital contributions of learning designers as sense-makers, strategic collaborators, and enablers of educational transformation, working across institutional and sectoral boundaries to make innovation possible.

This paper introduces the motivation-readiness matrix which we have found to promote constructive discussion about a teacher’s agency in influencing student engagement. We locate student motivation in previous theories of student

This paper introduces the motivation-readiness matrix which we have found to promote constructive discussion about a teacher’s agency in influencing student engagement. We locate student motivation in previous theories of student motivation and performance and also adapt insights from situational leadership theory which uses a similar matrix plotting skill against will in recommending appropriate leadership styles. Using these theoretical frameworks, we aim to reframe engagement as a key role of the teacher rather than solely a responsibility of students, thereby increasing a teacher’s response-ability to their students. To provide ways for university teachers to better understand their students and use that knowledge to improve learning design, we delivered workshops helping teachers plot perceptions of student motivation against readiness. We then addressed difficulties of personalising the learning experience of students in different categories. Following our workshops, 94% of participants reported increased confidence in supporting learning or enhancing belonging. 77% of respondents reported implementing something new to respond to student motivations, with 90% of those saying they considered motivation and readiness of their students in tailoring this response. We are now supporting teachers to design digital and face-to-face learning experiences that emphasise their enhanced response-ability to their students.

The purpose of this study is to investigate how an AI-powered feedback tool can be effectively designed to enhance both student and educator feedback literacy, with the broader goal of supporting educability in higher education. Adopting

The purpose of this study is to investigate how an AI-powered feedback tool can be effectively designed to enhance both student and educator feedback literacy, with the broader goal of supporting educability in higher education. Adopting an action design research (ADR) approach, the study will be piloted in classroom settings across two trimesters at a large higher education institution in Australia in the later half of 2025. The tool is designed to assist educators in transforming their formative reflections into detailed, personalised feedback, and any findings during the study will inform the iterative development of the tool to align with contemporary pedagogical principles. Although the interviews and classroom pilots are yet to commence, the design-based research methodology allows early engagement with literature and problematisation, allowing the research team to co-design and refine feedback features in a starting prototype.

Healthcare simulation is increasingly adopting biometric monitoring to enhance learning outcomes, yet current implementations predominantly reflect western physiological norms and stress responses. This presents significant challenges in

Healthcare simulation is increasingly adopting biometric monitoring to enhance learning outcomes, yet current implementations predominantly reflect western physiological norms and stress responses. This presents significant challenges in diverse Australasian healthcare contexts where students and practitioners represent multiple cultural backgrounds with varying manifestations of stress, decision-making patterns, and learning preferences. This position paper examines the critical gap between culturally neutral biometric systems and culturally responsive educational practice, proposing a comprehensive methodological framework for developing inclusive adaptive simulation environments that recognise cultural diversity in physiological responses and learning behaviours through systematic participatory design approaches.

The rapid emergence of generative AI (GenAI) in higher education offers transformative opportunities but also significant pedagogical and ethical challenges. Unlike coordinated institutional responses to COVID-19, GenAI adoption has been

The rapid emergence of generative AI (GenAI) in higher education offers transformative opportunities but also significant pedagogical and ethical challenges. Unlike coordinated institutional responses to COVID-19, GenAI adoption has been slower and more fragmented, often relying on individual educators navigating an ambiguous institutional landscape without clear guidance. This preliminary qualitative study introduces a five-stage decision-making framework, developed from a thematic literature review and interviews with early GenAI adopters across all six faculties at a large research-intensive university. The framework foregrounds reflective practice, ethical considerations, educator identity, and AI literacy as key factors shaping context-sensitive decisions about GenAI integration. Preliminary findings highlight the critical influence of institutional culture and leadership on educator practice. They also underscore the need for explicit institutional ethics frameworks that address data privacy, algorithmic bias, and pedagogical integrity. The model offers a practical, staged approach to support thoughtful decision-making, with broader applicability for informing institutional policy and guiding faculty-level strategy in this evolving context.

Design thinking (DT) has variously been described as a mindset, a process and a toolkit for approaching problem-solving. Online education is increasing in prevalence, particularly among mature-age student cohorts. This paper presents a

Design thinking (DT) has variously been described as a mindset, a process and a toolkit for approaching problem-solving. Online education is increasing in prevalence, particularly among mature-age student cohorts. This paper presents a case study in which design thinking methodology was used for the purposes of course improvement and curriculum renewal in an online postgraduate law course. DT tools such as empathy interviews, customer journey maps and personas were used in the initial phases of the project. The tools offered benefits in conducting rapid thematic and cluster analysis to better define problems extant in the program and inform the prioritisation of a sustained program of iterative improvements. Implications for practice or policy: DT tools offer an effective and sustainable method to: Identify, define and prioritise improvement opportunities in existing higher education programs within institutional resourcing constraints; Embed student collaboration and co-creation in designing improvements to student experience in online education; and Address attrition risk factors through adaptable and responsive methods that privilege student voice.

Group work is widely used in tertiary education to develop collaboration, develop teamwork and communication skills, and manage large class sizes. It supports active learning and often leads to higher engagement and improved learning

Group work is widely used in tertiary education to develop collaboration, develop teamwork and communication skills, and manage large class sizes. It supports active learning and often leads to higher engagement and improved learning outcomes. Yet creating truly diverse teams at scale is a major combinatorial challenge. We evaluated a diversity maximising random grouping strategy in a third-year Project Management course. A qualitative analysis of the intervention indicates that this approach helped students meet new peers, form friendships, gain confidence to speak up, and enhance peer learning. Furthermore, the randomisation was positively received by students and was consciously perceived as a benefit. This simple, sustainable practice can be used by educators to create resilient and inclusive learning environments.

Self-regulated learning (SRL) is essential for academic success, yet many learners struggle to plan, monitor, and reflect their learning processes without support. Large Language Models (LLMs) offer opportunities for real-time

Self-regulated learning (SRL) is essential for academic success, yet many learners struggle to plan, monitor, and reflect their learning processes without support. Large Language Models (LLMs) offer opportunities for real-time, personalised learning guidance, but cloud-based deployments raise privacy and trust concerns. This pilot study investigates the feasibility of delivering SRL support through a locally deployed, privacy-preserving chatbot. Using a design-based research approach, we co-designed a chatbot platform with sixty-one university students and conducted a two-week field study with seven participants using both local (offline) and cloud-based (online) modes. Mixed-method findings indicate that the chatbot successfully prompted higher-order SRL activities such as goal setting and reflective monitoring in authentic study sessions. Participants reported greater trust when using the fully local LLMs due to data remaining on-device. However, the local LLMs demonstrated much slower response times and occasional inaccuracies, highlighting privacy-performance trade-offs. This research demonstrates the potential of locally deployed, privacy-preserving, human-centred AI to support SRL and offers empirical insights into the benefits and limitations of deploying LLMs on small-scale local devices in educational contexts.

This paper investigates how students in different performance groups engage with and perceive feedback within a large postgraduate information technology project management unit at an Australian university. Using data from 228 students who

This paper investigates how students in different performance groups engage with and perceive feedback within a large postgraduate information technology project management unit at an Australian university. Using data from 228 students who voluntarily participated in a feedback analytics tool, named PolyFeed, the study examines patterns of feedback annotation, reflective note-taking, and action plan creation. The findings reveal that while feedback quality is consistently rated highly across all groups, lower-performing students participate less and are less likely to act on feedback. In contrast, higher-performing students demonstrate greater sense-making and proactive use of feedback. The study highlights the importance of designing feedback systems that scaffold all aspects of feedback literacy, particularly for students at risk of disengagement, to promote equitable and meaningful learning outcomes.

Work-integrated learning (WIL) has become central to higher education reform, yet its implementation remains inconsistent across disciplines, with evaluation often limited to surface metrics rather than cognitive depth. This study explores

Work-integrated learning (WIL) has become central to higher education reform, yet its implementation remains inconsistent across disciplines, with evaluation often limited to surface metrics rather than cognitive depth. This study explores how WIL is embedded within creative undergraduate programs of School of Communication & Design (SCD) at RMIT Vietnam – an international branch of a prestigious Australian university, responding to calls for greater curriculum-employability alignment in higher education. Drawing on transformative learning and authentic learning theories, and using Bloom’s revised taxonomy as an evaluative lens, this research systematically maps WIL integration across 153 courses in three curriculum layers—course learning outcomes (CLOs), assessments, and course materials. Quantitative analysis of action verbs and learning activities reveals that while WIL is moderately represented in CLOs and assessments, significant gaps exist in instructional delivery, particularly in early-year courses. The findings suggest a concentration of cognitively complex WIL in capstone experiences, with limited scaffolding earlier in programs. The study offers a replicable evaluative framework and advocates for intentional curriculum co-design involving educators and industry. This contribution informs pedagogical, institutional, and policy strategies to support more authentic, scaffolded, and equitable WIL integration.

What if we could simulate a learning environment like a living, evolving ecosystem? Our research views education as a complex adaptive system and applies methods from computational science to study it. We build agent-based models that

What if we could simulate a learning environment like a living, evolving ecosystem? Our research views education as a complex adaptive system and applies methods from computational science to study it. We build agent-based models that simulate how learning happens in classrooms, drawing on both quantitative and qualitative data. These models allow us to run simulations that are updated with real classroom data over time. This helps us explore questions such as: “Which teaching methods best support student learning and knowledge transfer?” Our modelling suggests that Productive Failure may be especially effective in promoting deeper learning and transfer. We conclude by discussing how these insights highlight the potential of computational modelling in education research.

This paper presents preliminary findings from the development and deployment of Mattie, an AI-powered chatbot tutor designed to support first-year university students in a business mathematics course. Motivated by concerns about students’

This paper presents preliminary findings from the development and deployment of Mattie, an AI-powered chatbot tutor designed to support first-year university students in a business mathematics course. Motivated by concerns about students’ reluctance to seek help, particularly in public forums where they fear appearing ‘stupid’ in front of peers or instructors, Mattie.AI was created to provide personalised and course-specific learning assistance on demand 24/7. Unlike general-purpose chatbots, Mattie.AI was built using a Retrieval-Augmented Generation (RAG) architecture, allowing it to generate responses based on course-specific learning materials. The underlying chatbot design was Socratic, focused on helping students through guided questioning instead of simply providing answers. Evaluation of the first deployment combined chatbot interaction data with student feedback from a usability and satisfaction survey to provide insights into its functionality as well as patterns of student engagement. Results suggest that subject-specific chatbots offer a promising supplement to traditional support structures, but students need to be supported to use them effectively. Future work in this space of AI-enhanced education technologies will refine Mattie’s capabilities and explore its impact on learning outcomes and help-seeking behaviours across different student cohorts.

As Generative Artificial Intelligence (GenAI) rapidly enters higher education, its implications for assessment and feedback require urgent, practice-informed exploration. This study investigates how educators at UNSW - a large Australian

As Generative Artificial Intelligence (GenAI) rapidly enters higher education, its implications for assessment and feedback require urgent, practice-informed exploration. This study investigates how educators at UNSW - a large Australian university - are engaging with GenAI in their teaching, drawing on data from the institution’s "Postcards of Practice" initiative; a short, reflective narratives of GenAI use in assessment contexts. Through a qualitative interpretive methodology, including thematic analysis of interviews and postcard content, the research explores educators’ experiences, comfort levels, perceived challenges, and support needs. Findings are expected to reveal diverse levels of GenAI adoption shaped by disciplinary context, ethical concerns, and usability perceptions. The study also anticipates a strong call for targeted professional development, institutional guidance, and customisable GenAI tools. This research foregrounds educators’ micro-level decision-making to contribute to the growing body of work on formative assessment, feedback literacy, and ethical AI integration. It positions GenAI not as a threat, but as an opportunity to reimagine assessment as a dialogic, developmental process.

This paper examines challenges implementing co-design practices in a Vietnamese campus of an Australian University and presents a tailored professional development approach addressing foundational collaboration understandings within

This paper examines challenges implementing co-design practices in a Vietnamese campus of an Australian University and presents a tailored professional development approach addressing foundational collaboration understandings within Vietnamese cultural contexts. After three years of operation, our learning design team faced persistent issues co-designing learning experiences with academics, revealing a fundamental misunderstanding of co-design principles within the team itself. Initial interventions failed because they employed didactic approaches that violated the collaborative principles they aimed to teach. Through practice-led research combining practitioner inquiry, collaborative auto-ethnography, and retrospective sensemaking, we identified two dysfunctional power dynamics: "power-for" relationships where learning designers acted as service providers, and "power-over" dynamics where designers dominated processes, rather than the "power-with" approach needed for true collaboration. Cultural factors, including Confucian values emphasising hierarchy and examination-oriented traditions, compounded these challenges. Our "Walking the Talk" intervention employed experiential role-playing activities, positioning learning designers as clients in interior design scenarios. This method successfully disrupted habitual practices, enabling participants to embody co-design principles rather than merely understanding them theoretically. Findings demonstrate that authentic collaboration requires deliberate cultivation in Vietnamese contexts, where social-political arrangements essential to co-design do not emerge organically. This research offers strategies for developing collaborative capabilities in hierarchical cultural contexts.

Generative AI-powered conversational agents represent new opportunities for personalised learning in higher education, but institutional adoption requires careful support and professional development. This paper presents a case study of a

Generative AI-powered conversational agents represent new opportunities for personalised learning in higher education, but institutional adoption requires careful support and professional development. This paper presents a case study of a collaborative trial involving twenty-six university staff who created and tested learning agents using the Cogniti platform over six months in 2024. Participants developed twenty agents across multiple disciplines, with nine successfully deployed to teaching practice. Survey results showed exceptional satisfaction rates, with all participants rating the platform as easy to use and 95% willing to recommend it to colleagues. Qualitative analysis identified benefits including scalable personalised feedback, enhanced student engagement, and flexible learning scenarios that would otherwise require significant staff time and resources. Participants emphasised that success depends heavily on developing prompting skills and understanding AI limitations. Critical success factors included expert support from learning designers acting as 'critical friends' and opportunities for interdisciplinary learning. Results suggest that well-designed collaborative trials can overcome barriers to AI adoption in higher education by providing necessary training, support, and community while ensuring pedagogically sound implementation that maintains quality learning experiences.