Since OpenAI’s release of ChatGPT (November 2022), generative AI models have proliferated and evolved rapidly. Beyond chat-based interactions and text generation, newer multi-modal and reasoning models incorporate image, video and audio generation and interpretation, as well as citation tools. These tools are increasingly embedded into mainstream productivity systems (e.g., Copilot in the Microsoft suite), lowering barriers to everyday use. The widespread availability of AI tools has had a profound impact on education. In Australia, the AI disruption has prompted the higher education sector to rethink existing assessment practices to mitigate misuse while adopting approaches that support the ethical, critical, and responsible use of AI (Lodge et al., 2023). As Huijser et al. (2024) explain, the challenge for academics and professionals supporting teaching and learning in higher education is not just to build technical proficiency with these tools, but also to build staff capabilities to critically engage with AI’s complexities and make informed decisions on its appropriate use in teaching contexts (Markauskaite et al., 2022). The project aims to design and iteratively refine a layered professional development (PD) suite to support staff AI literacy, defined as the capabilities enabling individuals to critically evaluate, communicate, and collaborate with AI (Long & Magerko, 2020). In this design case, I ask: How can responsive PD scaffold staff adaptability, critical judgment, and ethical engagement with AI? This approach responds to critiques that Technology Enhanced Learning (TEL) PD initiatives primarily focus on knowledge and skill acquisition and to calls for PD that goes beyond tool proficiency by intentionally designing for “learning about AI…, learning with AI…, and learning for human-AI collaboration” (Carvalho et al., 2022, p.2). The PD suite iterates an earlier pilot approach to support staff AI literacy at the same institution (Tibbs et al., 2024). This presentation focuses primarily on the workshops, which were delivered in six cycles (2024-2025). The 2024 workshops provided opportunities for first encounters with AI, tool comparisons, ethical discussions, and assessment design considerations. By 2025, informed by facilitator observations and reflections, transcripts, participant artefacts, evaluation survey responses, evolving AI technologies, and institutional infrastructure, the workshops were iteratively redesigned across cycles, alongside an updated self-paced module. The 2025 workshops were underpinned by socio-constructivist principles: more space was created for dialogue, critical evaluation of AI outputs was emphasised, and collaborative peer design challenges and the crafting of contextually relevant agents for practical use were included. They also incorporated a critical pedagogical orientation and elements of transformative learning, where staff interrogated assumptions about AI through engagement with ethical and Aboriginal and Torres Strait Islander perspectives, and explored their professional obligations to support students’ AI literacy development. The next planned layer in the suite, an AI in Learning and Teaching micro-credential, is currently in development. The evidence-driven, situative approach is grounded in educational design research (McKenney & Reeves, 2018) and design-based professional learning (Friesen & Brown, 2022); engagement and evaluation data from each workshop cycle informed the next cycle’s design. The PD suite is positioned to support staff’s critical and adaptive expertise, not just technical proficiency, to capably respond to the opportunities and uncertainties brought by AI (Markauskaite et al., 2022). Its layered architecture, from low-stakes self-paced exploration to applied collaborative workshops, and soon to reflective micro-credential, supports different starting points and scaffolded progression, enabling adaptation for institutions with different resource constraints. For example, a train-the-trainer model could be adopted so that tailored, discipline-specific workshops drawing on structured dialogue and critical reflection can be facilitated by school-based champions. This case offers insights into how this PD model can create conditions for adaptive and critical AI literacy development for university educators, beyond one-off technical upskilling (Friesen & Brown, 2022). It underscores the value of design interventions attuned to shifting teaching and learning ecologies, building staff capabilities to both create with and critique AI. The presentation will illustrate these design cycles, share facilitator reflections and participant artefacts, and outline future directions.

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.

Quality feedback is crucial to student success and learning experience; yet higher education continues to struggle with the provision of effective feedback. Scholarship in feedback studies has shifted from viewing feedback as a product transmitted from teachers to students, to promoting it as a two-way process in which students and teachers engage in interactive dialogue (Yang & Carless, 2013). However, predominant feedback practices still follow a one-way paradigm, with teachers having very limited understanding of how students engage with feedback. This is partly due to the difficulty in tracking ways learners interact with feedback. Consequently, there is a lack of support for students to benefit from feedback and for teachers to improve feedback strategies. To bridge the gap between students and teachers in a feedback process, we propose feedback analytics as a solution. Feedback analytics collects, analyses, and reports on students’ interactions with feedback. Such data-based insights have great potential to scaffold the development of students’ feedback literacy, manifested in active involvement in seeking feedback, interpreting it, acting on it, and regulating the emotions that may arise during feedback processes (Carless & Boud, 2018). By capturing, analyzing, and reporting data about learners’ interactions with feedback, feedback analytics can enhance reflections among learners on their learning progress and teachers on ways to support learners. In this presentation, we introduce a novel feedback analytics system that builds on key feedback theories including feedback literacy (Carless & Boud, 2018), dialogic feedback (Yang & Carless, 2013), and learner-centred feedback (Ryan et al., 2023). The tool can: a) support students to make sense of feedback and act on it; and b) support teachers to track student interactions with feedback and provide targeted support. The system contains a student-facing tool and a teacher-facing tool, both leveraging AI technologies to support key functionalities. The student-facing tool enables students to highlight and label feedback, create reflective notes and action plans, seek further explanations from Generative AI or teachers, and track their strengths, weaknesses, and action progress across multiple assessments and courses. The teacher-facing tool supports teachers to enhance feedback quality by assessing the alignment of their feedback with learner-centred principles and suggesting ways to improve it. It also helps teachers shape feedback strategies by providing data about students’ interactions with feedback through the functionalities mentioned above, in addition to students’ feedback requests and perceptions of feedback usefulness. The tool has been piloted in both lab and authentic learning settings, receiving positive feedback from educators and students on its potential to transform feedback practice, while noting the need to balance human judgement with AI suggestions.

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 proof-of-concept study explores the feasibility and usability of an interactive curriculum mapping dashboard designed to support informed decision-making around ophthalmology trainee rotations. Developed using data from the Royal Australian and New Zealand College of Ophthalmologists’ eDiary (surgical logbook) and Curriculum Component Survey, the dashboard allows stakeholders including trainees, supervisors, training networks, and the college to visualise curriculum coverage and identify potential gaps in clinical learning across training sites. Two key datasets informed the project: (1) 2023–2024 eDiary data, selected due to improved completeness following the introduction of required minimum procedures, and (2) 2023 Curriculum Component Survey data, which captured the type (Observe, Assist, Perform) and frequency (Frequent, Moderate, Occasional) of curriculum exposure reported by training sites. Data were integrated and visualised using Microsoft Power BI, with dashboard prototypes tested by college staff, Fellows, and committee members. The results highlighted substantial variation in clinical exposure across training posts and demonstrated how dashboard filters could assist in aligning individual trainee needs with available learning opportunities. User feedback confirmed the dashboard’s value in supporting planning and transparency, while also emphasising the importance of data accuracy and quality to ensure trust and usability. Overall, this study demonstrates the potential of a curriculum mapping dashboard to enhance data-informed training decisions in specialty medical education.

In an era of accelerated technological and societal change, the employability of graduates is an urgent concern for tertiary education institutions (Clarke, 2018). Graduates are increasingly expected to demonstrate teamwork, emotional intelligence, and lifelong learning skills alongside disciplinary expertise (Haselberger et al., 2012; Moore & Morton, 2017). However, evidence shows a lack of transferable skills among new graduates (Hurrell, 2016), and many curricula still lack explicit integration of these capabilities. In response to calls for the embedding of transferable skills into undergraduate curricula, the first-year subject Applied Psychology (PSY1APP) was recently developed at La Trobe University to develop work-ready competencies in psychology students. PSY1APP incorporates a range of assessments (including an unconventional reflective e-portfolio) which facilitate personal and professional growth and align with the competency-based learning models recommended in psychology education (Jhangiani & Hardin, 2015; Naufel et al., 2019). The subject also enhances engagement and authenticity through a variety of innovative digital learning tools – such as video interviews with psychology alumni embedded into the Learning Management System and the e-portfolio assessment – which further demystify career pathways and model diverse applications of psychology skills and knowledge. A qualitative study was conducted to explore students' engagement with PSY1APP and obtain feedback as part of a co-design process (Cooper & Krishnan, 2020). It comprised four focus groups with a total of 14 participants (10 women and 4 men, aged between 18 and 52 years, Mage = 31.6, SDage = 12.1) who had recently completed PSY1APP at La Trobe University. Focus groups had an average duration of 64 minutes and were conducted 3-5 weeks after the end of semester. Reflexive thematic analysis (Braun & Clarke, 2023) resulted in three broad themes, each comprising between two and three subthemes. 1) Personal Development: students described transformative impacts on their self-knowledge, relationships, and perspective. The subject promoted metacognitive thinking and empowered students to apply psychological principles to their own lives, relationships, and wellbeing. 2) Expanding Horizons: the subject offered insight into the ‘real world’ of work in psychology, exposing students to diverse applications and helping them reimagine possible futures, moving beyond traditional pathways such as clinical psychology. 3) Tension Between Breadth and Depth: while some students appreciated the wide scope of topics, others expressed a desire for deeper engagement with specific content, highlighting the ongoing challenge of designing adaptable learning environments that are both inclusive and personalised. These insights have prompted improvements to the PSY1APP curriculum and have positioned PSY1APP as a key subject in the current employability-focused re-design of the undergraduate psychology course at La Trobe. The results of this study show that through authentic and digitally enhanced curriculum design, the building of employability skills need not be restricted to placements and final-year subjects. Instead, digital tools such as reflective e-portfolios and multimedia case studies can support students to become resilient, future-focused learners. PSY1APP offers actionable insights for educators across disciplines wishing to embed applied skill development and innovative digital assessment types into undergraduate curricula for deep learning, transformative change and student wellbeing.

Academic integrity is a cornerstone of ethical scholarship and fair and credible higher education. However, many students – particularly those from underrepresented backgrounds (Bretag et al., 2019; Devlin et al., 2018; Dockery et al., 2016; Webb et al., 2024) such as international, low socio-economic, and regional, rural and remote cohorts - struggle to understand its principles and institutional expectations (Birks et al., 2020; Locquiao & Ives, 2020, 2024; McCabe et al., 2012; Waltzer & Dahl, 2023). These challenges are often shaped by systemic barriers, which can lead to disproportionate rates of unintentional misconduct, disadvantage (Dockery et al., 2016; Webb et al., 2024), and poorer learning experiences. Existing academic integrity education often fails to address these inequities (Bretag et al., 2019; McCabe et al., 2012) relying on generic, compliance-focused, one-size-fits-all approaches that do not fully meet the needs of these diverse learners. This project addresses these gaps by creating interactive digital resources that promote ethical decision-making and academic resilience for all students. Unlike traditional technology enhanced learning models, it embeds inclusive design and student co-creation at its core. Students were engaged as active partners in the design of these resources, ensuring their voices shape both pedagogical and practical decisions. Grounded in constructivist learning theory, this project drew on inclusive education design, and students-as-partners frameworks (Cresswell, 2011; Gravett et al., 2020; Tennyson, 2010). Constructivist principles informed the use of authentic, scenario-based learning to support ethical reasoning, while iterative co-design cycles, guided by the students-as-partners approach, ensured the materials reflected diverse perspectives and needs. The project was implemented across multiple phases, using both quantitative and qualitative methods to investigate students’ knowledge and understanding of academic integrity. Results from an online questionnaire showed that while most students could correctly identify plagiarism, fewer understood subtler concepts such as paraphrasing and correct referencing. Focus group discussions further highlighted varied experiences with academic integrity education. Students described current online modules as outdated or unengaging and expressed a need for more practical resources as well as ongoing support to navigate academic integrity expectations. The previously outlined theoretical foundations, together with the collected data, guided the development of scenario-based resources, including case-studies, interactive digital tools such as an AI chatbot, and staff guides and training material. Student co-creators from diverse backgrounds collaborated with academic staff to design, refine and test these materials for accessibility and relevance. Preliminary findings highlight that student co-created materials improved students’ understanding of academic integrity and increased their confidence in ethical academic practices. Staff also report greater ease in integrating practical academic integrity concepts into their teaching. This work demonstrates how academic integrity education can move beyond a ‘one-size-fits-all’ approach, offering a scalable, equity-driven model – supported by innovations such as interactive AI - that better serves the realities of today’s diverse higher education landscape.

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.

Belonging is a fundamental human need that motivates individual pursuits (Baumeister & Leary, 1995). In higher education, it is closely tied to affective engagement, playing a critical role in sustaining student retention and participation (Strayhorn, 2019). Compared to undergraduate students’ belonging, graduate students’ experiences remain underexplored (Matheson & Sutcliffe, 2018). This gap is particularly relevant for MBA students, who often invest financially, juggle multiple commitments, and adapt to new academic environments. While belonging is increasingly recognised as multifaceted (Gravett, Ajjawi, & O’Shea, 2023), research indicates that graduate students experience this primarily in the academic domain (Briant Spratling & Valdovinos, 2022). A low sense of belonging can lead to disengagement and reduced satisfaction with the course (Lee & Anantharaman, 2015). While induction programs may foster early belonging (e.g., Matheson & Sutcliffe, 2018), its dynamic nature (Allen et al., 2024) presents challenges for supporting personalised beyond initial stages. In this poster, we describe a tailored email intervention to support MBA students’ belonging by providing a schedule of timely and personalised communications, tailored to students’ goals for the program. Students in the program can tailor their curricular and extra-curricular experience based on their unique career and personal goals. Given the diversity of student pathways to the program and associated aspirations, there is substantial scope to tailor the MBA experience. Accordingly, students require support to understand the scope of choices available and make appropriate choices accordingly. This intervention was carried out over two semesters of an MBA program at the University of Technology Sydney. An initial survey was disseminated near the start of the academic year to all students in the program, to solicit their goals for enrolling in the MBA program. The survey was framed against students’ general career aspirations, disciplinary specialisation interests, profession networking interests, communication development aspirations, employment goals, mentoring interests, views on contributing to positive societal outcomes, entrepreneurial interests and international exposure interests. Following this, three personalised emails were sent over each semester via the automated feedback tool, OnTask (Pardo et al., 2018), tailored to students’ responses to the initial survey. 70% of the Autumn cohort (total enrolled = 449 students) and 27% of the new Spring cohort (total enrolled = 56) responded to the initial survey and therefore received the personalised emails. Students were invited to complete a short survey to provide feedback about the personalised emails, and to participate in an interview about their experience. Students reported positive perceptions of their personalised support, which provided timely academic guidance and enhanced the feeling of being valued. An important finding was that prior educational experiences influenced students’ engagement with their personalized advice: newcomers desired more structured mentorship, while returning students more effectively navigated opportunities to enhance integration and belonging. These findings highlight the nuances of belonging faced by graduate students, especially those from international backgrounds. In this poster, we present the challenges and key learnings from this pilot implementation of personalising support for belonging at the program level.

Generative artificial intelligence (GenAI) is rapidly reframing academic practice, yet staff confidence to harness its potential remains uneven. Many university staff indicate that a “lack of familiarity with the technology, uncertainty about its use, or a lack of time to engage, contributed to their reluctance to use AI tools for their work” (McDonald, 2024). Building this confidence demands accessible opportunities that honour diverse starting points, and courageous experimentation which normalises trial-and-error—two of the principles guiding the University of Newcastle’s GenAI response. Our GenAI Tinker Workshops operationalise these principles by offering low-stakes, experimentation-based experiences where educators co-explore AI tools, reflect on ethical dimensions and iterate on real teaching artefacts. Playful, informal learning environments may lower cognitive load and heighten curiosity by reducing the focus on serious workplace implications (O’Hara & Lo, 2025). Tinkering studios, for example, cultivate “permission to fail” that supports creativity and innovation (Henriksen, 2021). The informal, low-stakes format of the sessions promoted engagement, echoing Worth’s (2024) assertion that “AI playgrounds” backed by appropriate guidance can support learning. Responding to TEQSA’s call for structured capacity-building around GenAI (TEQSA, 2024), Learning Design & Teaching Innovation designed monthly Tinker Workshops capped at roughly ten participants. Each 60-minute session invited academics to “tinker” with tools like ChatGPT, NapkinAI Copilot, NotebookLM and Gamma to draft real artefacts (course plans, presentations, podcasts, images, etc) for use in their teaching activities. Facilitators guide the participants through a simple introductory activity and focus on identifying opportunities to promote discussion in the group. Evaluation of the workshops, through informal participant feedback and in-session observations, showed an appreciation for this approach. Participants reported increased confidence not only with the tools but with navigating rapid change more broadly, citing reduced anxiety and a stronger sense of community. Participants noted possible operational benefits identified by exposure to various AI tools (example comments – “this will save me so much time” and “That just took about 20 seconds, it would take me three days to do the same.”). They also developed insights beyond the immediate operational focus of the activity (example comments – “I’d never thought of structuring my course that way, it’s really clear” and “This has really helped me think about how I use images and graphics in my lectures.”). Further, the informal nature of the sessions, with facilitator positioned as fellow learner rather than expert instructor supported honest discussion and debate around the implications (both positive and negative) of GenAI (example comments – “it’s just good to be able to talk about this stuff, and know everyone is in the same boat” and “I kind of knew that not all AI is not bad, but have not had the time to learn more.”). End of session discussion is guided towards awareness of the fallibility of GenAI outputs, and the importance of human expertise in co-creating learning experiences with GenAI. By embedding accessible, iterative, low-stakes Tinker Workshops alongside more traditional GenAI initiatives, we aim to directly tackle the challenge of uneven confidence driven by unfamiliarity, uncertainty and limited time. The format normalises trial-and-error, scaffolds ethical reflection and focuses “AI playground” tinkering on real world use cases—delivering immediate efficiency gains while supporting consideration of broader pedagogical impacts. Participants report reduced anxiety, stronger professional community and increased confidence in decision making around the use of GenAI. In short, structured opportunities to experiment safely are building adaptable educators who can collaborate with AI responsibly, meeting TEQSA’s call for capability-building and strengthening resilient, human-centred learning.