As Generative AI (GenAI) technologies become more commonplace in educational settings, its far-reaching impact on assessment, teaching and learning can be felt across all educational sectors. In terms of assessment, dealing with a
As Generative AI (GenAI) technologies become more commonplace in educational settings, its far-reaching impact on assessment, teaching and learning can be felt across all educational sectors. In terms of assessment, dealing with a technology that can easily produce written work such as an essay with high levels of accuracy and written style, teachers have been challenged in navigating this new space. Two challenges exist – how teachers can effectively share specific, appropriate and permitted GenAI use for a task, and how students may share with their instructors how they have used it. Previous models of GenAI integration into assessment generally exist on a spectrum of total and free GenAI use to this set of technologies being essentially banned (see AIAS scale by Perkins et al., 2024), with little specificity. PETRA AI (PErmissive and TRAnsparent) is a single page web application that addresses these challenges by providing teachers a way to permit specific and fine-grained use of GenAI for an assessment task in the form of a digital badge and also allows students to share how they used AI for a task to ensure compliance and transparency. This badge can then be downloaded as an image or embedded into an online learning platform to provide more transparency for all parties.
The rapid evolution of digital tools and heightened expectations for technology-enhanced learning are driving academics to continually upskill and adopt emerging digital tools in their teaching. However, the support systems surrounding
The rapid evolution of digital tools and heightened expectations for technology-enhanced learning are driving academics to continually upskill and adopt emerging digital tools in their teaching. However, the support systems surrounding these tools have often been fragmented, reactive, and insufficiently integrated into our institutional structures. Offering limited opportunities for staff to build the confidence and capability needed to use educational technologies effectively and independently. Moreover, the absence of systemic coordination contributes to staff anxiety and disengagement. For example, teaching staff reported feeling overwhelmed with troubleshooting the Moodle gradebook during peak assessment periods, uncertainty about who to contact and frustration with inconsistent, incorrect or delayed responses from vendor support. Those less confident with the technology often reverted to manual workarounds or avoided adopting new tools and features altogether. To address these challenges at our university, we developed the Cyclic EdTech Support Model: a unified, collaborative framework that shifts from traditional siloed EdTech support to a connected, scalable and feedback-driven ecosystem. Informed by constructivist learning theory, socio-technical systems thinking and continuous improvement principles, the model focuses on moving from dependency to empowerment. Giving staff the confidence to navigate the EdTech ecosystem independently while still feeling supported. It emphasizes community learning, distributed expertise, and the importance of feedback loops in complex systems to enable scalable innovation. It operates on six interconnected elements: Capacity-building at the Start: where staff and students are onboarded through training and workshops(e.g. Hands-on workshops on key edtech tools prior to the start of the term for staff, Orientation Week demo for students) Self-help Enablement: using digital resources and chatbot support to reduce barriers encourage just-in-time help-seeking(e.g. Scribe step-by-step guides on web resources which can be updated real-time, Chatbots directly accessible within Moodle) First-Level Internal Support: via a new in-house team replacing the previous vendor-based service, staffed by knowledgeable professionals who provide fast, contextual troubleshooting and triage (e.g. In-house EdTech level 1 team supporting via phone, chatbot escalation and webform) Second-Level Expert Intervention: through advanced troubleshooting, trend analysis, local faculty collaboration and resource development(e.g. Team of technical SMEs, Power BI reports on inbound enquires) Systemic Escalation: where vendor-facing teams manage issue resolution and patch deployment(e.g. Coordination with Moodle, Turnitin, Echo360 vendors to resolve bugs) Ongoing Improvement & Automation: to streamline processes and maintain up-to-date knowledge base and chatbot responsiveness(e.g. Transition from email enquiries to webforms and then to PowerApp, monthly update of Chatbot knowledge base, automated notification on system/ reported issue updates) To ensure long-term sustainability and support for new tools, the model is also incorporated into a standardised university EdTech change management framework via a project-to-operations checklist, allowing newly introduced EdTech platforms and features to be transitioned into ongoing support with aligned training, documentation, and escalation pathways. Since implementation, the model has significantly improved coordination, service delivery, and confidence among staff. It places user experience at the centre, drawing on automation, data trends and stakeholder networks to deliver more than just ‘support’ - it enables digital confidence and capacity across the university. User satisfaction has consistently exceeded 95%, based on post-support survey from over 7600 enquires in the last 24 months. Satisfaction was measured using standardised feedback form asking users to rate their experience on a 5-point scale. Feedback from users frequently described the service as approachable, prompt, knowledgeable, clear and solution oriented. This case exemplifies how Technology Enhanced Learning practices can be augmented through operational innovation by embedding collaboration, shared responsibility and upskilling into a sustainable support structure. Its platform-agnostic and modular design makes it readily transferable across tertiary contexts, particularly for institutions aiming to streamline support and foster digital independence among staff.
Higher education has recently experienced significant disruptions from the COVID pandemic to the rise of generative AI. In addition, a focus on inclusion and accessibility have required thoughtful and intentional responses to the design of
Higher education has recently experienced significant disruptions from the COVID pandemic to the rise of generative AI. In addition, a focus on inclusion and accessibility have required thoughtful and intentional responses to the design of quality learning. Technology plays a significant role in designing these responses and contributing to innovative learning experiences that prepare students for the real world (Pelletier et al., 2022). While these disruptions and changes are challenging, they provide opportunities to spotlight the valuable role of education and learning designers in supporting transitions to online learning, redesigning assessment and creating engaging online learning activities and environments (Bellaby et al., 2020). These (predominantly) professional staff are critical to the design and development of technology enhanced learning experiences (D’Souza, Luu & Cui, 2022, Altena et al., 2019, Halupa, 2019). However, despite their valuable contribution to this work, the contribution of education and learning designers remains largely hidden, undocumented and unacknowledged in the sector. Bisset (2018, p. 18) noted that “Educational designers themselves need support and professional development to identify and articulate the way in which their professional interactions at the personal, relational, and institutional level bring different dimensions to the role” and D’Souza et al. (2022) similarly highlighted the need for future research to identify the influence of educational designers. To address these identified needs, the Learning Design Evidence Framework has been developed to assist learning designers to support their value proposition in collaborations with academic teams, guide them to be more intentional in those collaborations, and provide a means to evidence to institutions the impact of good design in promoting quality teaching and learning. It is a means by which learning designers can ‘show’ what they ‘know and do’. (Boreland, Henry & Sharpe, 2025). This makes it an invaluable resource for leaders and managers of project teams or learning design units who can use the Framework to showcase the value, impact, and contributions of these teams or units in meeting the challenges currently faced in higher education and/or in advancing institution-wide goals. This session provides a brief overview of the Framework and a case study of how it is being implemented at one university. The session will challenge other team leaders and managers to think about how they can evidence and promote the value learning designers bring to the creation of quality learning for successful student outcomes in an era of continuous change.
Jimena de Mello Heredia, Michael Henderson, Margaret Bearman, Jennifer Chung, Tim Fawns, Simon Buckingham Shum, Kelly Matthews
The rapid integration of generative artificial intelligence (GenAI) into higher education has sparked debates about the future role of teachers (Chan & Tsi, 2024), including in providing feedback information to students. While GenAI offers
The rapid integration of generative artificial intelligence (GenAI) into higher education has sparked debates about the future role of teachers (Chan & Tsi, 2024), including in providing feedback information to students. While GenAI offers unprecedented accessibility and immediacy, this presentation argues that teachers' expertise remains irreplaceable in productive feedback – i.e., processes in which students make sense of information about their performance and use it to improve the quality of their work or learning strategies (Henderson et al., 2019, p. 1402). Drawing on a large-scale, cross-institutional survey involving 6,960 Australian university students (Henderson et al., 2025), this Pecha Kucha highlights students' perceptions of GenAI versus teacher feedback. The quantitative analysis revealed that nearly half of them (49.7%) reported using GenAI for feedback. However, they rated teacher feedback as more helpful and significantly more trustworthy. While 83.9% found GenAI feedback helpful, only 60.1% considered it trustworthy, compared to 90.5% who trusted teacher feedback. This trust gap may reflect the inconsistent quality identified in GenAI's feedback comments (Venter et al., 2024). The thematic analysis of 5,736 open-ended responses from students who used GenAI for feedback yielded 8,498 coded instances, revealing four interrelated characteristics in which teacher feedback was perceived as outperforming GenAI. Contextualisation and Relevance: Teacher feedback was perceived as more sensitive to specific assignment contexts (95.2% of 669 instances rated GenAI as less contextualised than teacher feedback) and more relevant to learning objectives (84.6% of 123 instances rated GenAI as less relevant). This contextual awareness enables teachers to identify what matters within disciplinary and course-specific frameworks. Reliability and Accuracy: Students perceived teacher feedback as significantly more reliable and trustworthy (95.4% of 1143 instances), reflecting teachers' ability to provide more trustworthy and accurate guidance without the hallucinations and factual inaccuracies that can appear on GenAI outputs. Relational Significance: Teachers offered more personal, connected feedback experiences (93.8% of 471 instances), providing the interpersonal recognition essential for productive learning relationships. This relational dimension cannot be replicated by GenAI’s algorithmic responses. Expertise: Students recognised teachers as more authoritative sources (88.2% of 119 instances), valuing their disciplinary knowledge and pedagogical understanding of student development trajectories. Students' evaluation of feedback is fundamentally shaped by perceptions of source credibility (Bearman et al., 2024), which may explain why students perceive teacher feedback as more trustworthy than GenAI's. Research demonstrates this selective engagement: uptake of content-focused GenAI feedback was considerably lower than form-focused feedback(Ziqi et al., 2024), suggesting students recognise GenAI's limitations for substantive guidance requiring disciplinary expertise. This translates into learning outcomes, with students not only perceiving instructor feedback as more useful but also demonstrating significantly greater lab score improvements than those receiving GenAI feedback (Er et al., 2025). GenAI may create opportunities for educators to focus on what they do best: providing expert, contextualised, and relationally-grounded feedback within authentic learning relationships. This potentially positions teacher expertise as increasingly valuable, with educators prioritising higher-level pedagogical responsibilities, such as developmental guidance, facilitating critical thinking, and disciplinary enculturation, while GenAI supports lower-level feedback processes, like grammar correction and initial draft review. Students appear to already recognise this distinction, trusting teachers for more substantive, transformative feedback while appreciating GenAI's supplementary role for immediate, accessible guidance.
This Pecha Kucha will explore how a large metropolitan Australian university is strategically investing in the pedagogically expertise of learning designers (Lodge, 2023) to lead institutional wide assessment transformation and artificial
This Pecha Kucha will explore how a large metropolitan Australian university is strategically investing in the pedagogically expertise of learning designers (Lodge, 2023) to lead institutional wide assessment transformation and artificial intelligence (AI) capability development. As AI becomes increasingly embedded in higher education (Perkins, 2024), institutions face mounting pressure to ensure the integrity and relevance of student learning outcomes. In response, learning designers are being placed across faculties to lead both systemic change and targeted support for educators, navigating a complex digital and cultural shift. Their nuanced understanding of discipline-specific needs enables them to co-design solutions that balance assessment security, AI literacy, and pedagogical integrity (Lodge, 2023). Applying a hub-and-spoke model that connects nuanced disciplinary expertise with a centrally driven strategy, the team of learning designers are collaborating with academics to drive change at scale and lead educational innovation. These designers work closely with teaching staff to adapt assessment practices in ways that are contextually relevant and pedagogically sound. At the same time, they are bridging institutional silos by co-developing cross faculty resources, contributing to academic integrity frameworks, curating and disseminating exemplars of effective practice, and facilitating professional learning. Their work includes mapping assessment against program outcomes, redesigning tasks for authenticity and AI resilience, supporting the development of academic and student AI literacy, and enabling scalable approaches to feedback and moderation. This coordinated approach ensures that local innovation aligns with institutional priorities, enabling meaningful and sustainable assessment transformation. This presentation will showcase one faculty’s approach which includes a scalable and sustainable series of workshops aimed at engaging hundreds of academic staff, delivered through multiple school-specific sessions, fostering collegial dialogue around AI, assessment design, and quality assurance, providing educators with the tools and confidence to adapt to a rapidly evolving landscape. In practice, participants are invited to critically evaluate the broader assessment architecture of their courses – asking whether tasks align with course and program learning outcomes, whether the chosen methods best capture intended learning and how principles like UDL and authenticity are embedded. These sessions also explore opportunities to meaningfully integrate AI, not as a threat but as a catalyst for pedagogical innovation. Drawing on the framing of AI as a “wicked problem” (Corbin, et al., 2025), educators are encouraged to shift from seeking perfect solutions to making continuous professional judgements in conditions of permanent uncertainty. This approach builds resilience – not just to AI, but to the future challenges yet to emerge – by treating assessment transformation as a strategic, adaptive and context-sensitive process (Dawson, et al., 2024). This institutional initiative demonstrates how human expertise, through collaboration, trust, and pedagogical design—can effectively guide AI adoption at scale. It also illustrates the vital role learning designers play in balancing institutional priorities with discipline-specific aspirations. Session attendees will gain insights into the power of strategic collaboration between central and faculty teams, and how learning designers are enabling ethical, innovative, and future-ready learning environments.
Abdullahi Chowdhury, Siamak Mirzaei, Mohammad Afikuzzaman
Intelligent tutoring systems (ITS) utilise artificial intelligence to deliver personalised educational content, guidance, and feedback. Recent advances in Generative AI (GenAI) have shown considerable promise in the development of ITSs
Intelligent tutoring systems (ITS) utilise artificial intelligence to deliver personalised educational content, guidance, and feedback. Recent advances in Generative AI (GenAI) have shown considerable promise in the development of ITSs, offering the potential to provide highly tailored educational experiences. However, current GenAI-based systems frequently suffer from critical limitations such as content hallucination, generation of overly complex responses, and misalignment with defined course objectives, undermining pedagogical effectiveness and risking academic integrity. Existing GenAI-powered educational tools often rely on vast, unfiltered internet-sourced training data, lacking the ability to restrict outputs exclusively to course-specific materials. This typically results in feedback that is either overly generic, misaligned with curriculum sequencing, or pedagogically inappropriate. Traditional ITSs, relying on rigid, rule-based structures, similarly face difficulties in capturing the nuanced, context-rich language typical in higher education environments. Research suggests potential solutions using context-free grammar (CFG) and predictive parsing techniques. Nevertheless, these methods have traditionally been limited to experimental laboratory environments, and their efficacy in comprehensive educational contexts remains underexplored. This research addresses these gaps by proposing a novel intelligent tutoring platform that integrates CFG and Earley’s parsing algorithm with a GenAI model specifically trained on curated, course-aligned content. Unlike traditional systems, our approach uniquely considers the student’s specific position within the course learning trajectory and their current understanding level, ensuring output is both cognitively appropriate and pedagogically relevant. For instance, if a student demonstrates difficulties in Week 6 activities, the system intelligently identifies underlying conceptual gaps originating from earlier weeks (e.g., Week 2) and delivers targeted, remedial content precisely aligned with their immediate learning needs. The integration of CFG with Earley’s parsing within a generative AI framework ensures pedagogical consistency, computational efficiency, and ease of integration with existing university infrastructure. By restricting content generation strictly to validated course materials, the system significantly reduces the risks associated with hallucinations and ensures alignment with educational objectives. A pilot study across two STEM courses within UniSA Online will evaluate this system’s effectiveness in providing accurate, relevant, and cognitively aligned content. Key research questions include: How effectively do GenAI-generated assessments and feedback align with course learning outcomes compared to instructor-authored materials? Does the integration of CFG and Earley’s parsing significantly mitigate hallucinations and curricular drift? To what extent can the system reduce academic staff workload related to assessment creation and personalised student feedback? Anticipated outcomes include improved alignment between assessment content and learning outcomes, enhanced personalised learning experiences, reduced academic workload, and valuable data for continuous course improvement. Deliverables encompass a fully operational tutoring platform, reusable grammar libraries, implementation guides, training resources, and scholarly publications. This project contributes significantly to Technology Enhanced Learning (TEL) by advancing theoretical and practical understanding of effective, course-aligned GenAI application. This project pioneers a new class of intelligent tutoring systems, course-aligned, hallucination-resistant, and responsive to individual learner trajectories. Its scalability and adaptability across diverse tertiary contexts underline its value for broad educational innovation, aligning strongly with UniSA’s strategic goals in fostering pedagogical excellence, integrity, and technological leadership in online education.
Healthcare training using simulation has experienced significant technological advancement, with immersive reality (XR) enhancements demonstrating impressive visual sophistication and procedural authenticity. However, this progress
Healthcare training using simulation has experienced significant technological advancement, with immersive reality (XR) enhancements demonstrating impressive visual sophistication and procedural authenticity. However, this progress conceals a critical misalignment between technological priorities and educational outcomes. Current XR healthcare education operates under what we term the "fidelity fallacy" - the assumption that high visual realism automatically translates to effective clinical preparation (Hamstra et al., 2014). This presentation challenges this paradigm, arguing that the industry emphasis on environmental authenticity has systematically neglected the affective dimensions essential for clinical competence. Contemporary research reveals that XR healthcare implementations predominantly focus on anatomical accuracy and procedural fidelity, while very few incorporate emotional regulation elements (Li, 2024). This disparity is particularly problematic given evidence that emotional resilience significantly impacts clinical performance and patient outcomes. Research consistently demonstrates that healthcare professionals and other stakeholders experiencing elevated stress show substantially decreased diagnostic accuracy and reduced communication effectiveness during critical incidents (Akhtar et al., 2025a). Yet existing XR systems present static, pre-programmed scenarios that fail to respond to learners emotional states, creating an authenticity gap between training and practice. The disconnect becomes more pronounced when examining real-world clinical demands. Healthcare professionals face numerous unexpected variations during critical events, requiring constant emotional regulation and adaptive decision-making. Current XR implementations, despite their visual sophistication, cannot adapt to individual emotional responses, limiting their effectiveness in preparing learners for these complex realities. Whilst many simulation centres collect biometric data, very few utilise it for real-time adaptation, representing a significant underutilisation of available technology (Akhtar et al., 2025b). This presentation advocates for a paradigm shift towards emotionally responsive XR systems that establish bidirectional relationships between learner and environment. Evidence from adjacent fields demonstrates the feasibility of such approaches - military training, aviation, and competitive sports have successfully integrated physiological monitoring with environmental adaptation (Yockey, 2023; Bernabei & Costantino, 2024). Healthcare education lag in adoption reflects not technological limitations but conceptual constraints rooted in the fidelity fallacy. The argument extends beyond individual learning outcomes to systemic healthcare implications. Studies show simulations with real-time biofeedback yield substantial improvements in stress management, decision accuracy, and learning transfer compared to traditional approaches (Farsi et al., 2021). These improvements translate directly to patient safety and care quality. Furthermore, cultural considerations add complexity often ignored in current implementations - research identifies significant variations in stress manifestation and decision-making patterns amongst healthcare students from diverse backgrounds, yet few XR simulations incorporate culturally specific elements (Zhang et al., 2024). The presentation introduces the Emotionally Responsive XR Clinical Environment (ERXCE) framework as a solution addressing these documented gaps. ERXCE establishes continuous feedback loops between physiological monitoring and adaptive scenario complexity, creating personalised learning experiences that prepare healthcare professionals for both technical and emotional challenges of clinical practice. By integrating affective computing with clinical simulation through multi-modal XR technologies, ERXCE addresses the critical gap between technical skill development and emotional resilience training. This work argues for a shift in clinical simulation for healthcare training from static representation to responsive adaptation that recognises the complex interplay between technical competence and emotional regulation in healthcare practice.
Generative artificial intelligence (AI) is reshaping assessment strategies in higher education. In units with online content especially, educators face increasing pressure to assess foundational knowledge with both authenticity and
Generative artificial intelligence (AI) is reshaping assessment strategies in higher education. In units with online content especially, educators face increasing pressure to assess foundational knowledge with both authenticity and integrity. Multiple-choice questions (MCQs), long valued for their scalability and efficiency, are now perceived as inauthentic and vulnerable to AI-enabled academic misconduct. A common response has been the abandonment of online MCQs. However, this risks leaving foundational knowledge unassessed, creating knowledge gaps and weakening the cognitive scaffolding essential for higher-order thinking and complex skills (Sweller, 2011). We present a strategy to reframe MCQs as both authentic and AI-aware instruments that support sustainable educator workload and motivate deeper learner engagement. Built on Action Mapping (Moore, 2017)—well-known in corporate instructional design, but under-utilised in higher education—we first shifted questions from rote recall to decisions anchored in real-world scenarios where foundational knowledge informs action. This results in authentic questions that help learners navigate real-world scenarios, outperforming typical people who do not possess knowledge or skills gained in a unit. Next, we added a novel layer to the framework to make the questions AI-resistant. This layer relies on inherent shortcomings of AI trained on large language databases, as well as entrenched reasoning biases common across LLM AIs. The result is a suite of questions that top-performing AIs consistently and confidently answer incorrectly. This design process led to what we call A+ MCQs—making questions that are: Applied: Rooted in discipline-specific, real-world decisions requiring knowledge-based judgement. Authentic: Reflective of lived or professional realities to increase relevance and engagement. AI-aware: Iteratively refined until AI responses consistently falter, ensuring human cognition drives success. A+MCQs were piloted in PSY2014: Mental Health in the Digital Age, a second-year undergraduate psychology unit. The result was a robust bank of A+ MCQs serving multiple purposes: reinforcing essential knowledge for complex tasks later in the learner’s journey, connecting knowledge with practical choices, and allowing for efficient automated marking without compromising assessment integrity or quality. These questions also clarify the current relationship between professionals and AI by illustrating to learners concrete instances where learners who retain and apply knowledge will outperform AI. The session will showcase how traditional MCQs were transformed into A+ questions, provide side-by-side comparisons, and share practical co-developing strategies using the TPACK lens. Attendees will leave with a design guide and tips to create authentic, AI-resilient assessments that maintain both pedagogical rigor and operational efficiency in a world increasingly shaped by AI.
How can higher educational educators break free from the isolation of online teaching and build meaningful professional connections? This presentation explores how an Australian University has adapted ‘Teaching Squares’ as initiative to
How can higher educational educators break free from the isolation of online teaching and build meaningful professional connections? This presentation explores how an Australian University has adapted ‘Teaching Squares’ as initiative to suit a digital environment, offering a flexible, scalable model for collaborative professional development among online educators. At its core, the initiative transforms solitary online teaching into a shared journey of observation, reflection, and growth. The purpose of this initiative is to address a persistent challenge in online education including lack of structured opportunities for peer interaction and reflective dialogue. Many online educators work in silos, rarely witnessing how others design or deliver their courses. The use of teaching squares program reimagines professional learning by bringing educators together through structured, non-evaluative peer observation which is designed not for critique, but for inspiration and self-reflection. Originally implemented in face-to-face university settings, Teaching Squares brings together three to four educators to observe each other’s teaching over a fixed period. In a digital space, instead of in-person classroom visits, participants share a curated selection of course artefacts including recorded lectures, forum threads, LMS navigation, announcements, and assessment materials. These are reviewed asynchronously, enabling participants to engage with rich teaching content at their own pace. This is then followed by a structured reflection and a virtual group debrief. This approach is underpinned by principles from the Scholarship of Teaching and Learning (SoTL), social constructivism, and collaborative learning theory. It recognises that peer-led reflection can deepen teaching practice and build collegial bonds, particularly when done in a psychologically safe, non-judgemental space. Rather than being evaluated, each educator reflects on their own practice after observing others, focusing on elements they found inspiring or thought-provoking. Participants report wide-ranging benefits including greater self-awareness, new pedagogical ideas, and a stronger sense of professional belonging. From improving student engagement strategies in discussion forums to rethinking assessment clarity, many have implemented changes directly influenced by what they observed. For some, the experience sparked longer-term collaborative partnerships and ongoing pedagogical conversations. What makes this initiative particularly relevant to today’s online education landscape is its accessibility and scalability. By removing geographical constraints and time pressures, the program invites broader participation including casual and early-career educators who are often left out of traditional professional development opportunities. The asynchronous format also allows for deeper, repeated exploration of teaching materials, leading to more thoughtful reflection. Preliminary results from our ongoing evaluation show strong participant satisfaction and increased teaching confidence. These early findings suggest that Teaching Squares doesn’t just support individual growth, it fosters a culture of shared learning across the institution. This presentation will walk through the process of adapting and implementing Teaching Squares in an online education setup. It will showcase real artefacts, share participant views and reviews, and offer practical guidance for replicating the model in other online or blended environments. Ultimately, it invites educators to rethink professional development not as a solitary pursuit but as a shared, reflective, and transformative practice.
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
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 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
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.
A national and global gap in transferrable work-ready skills among new graduates has led to calls both internationally (Strohmetz et al., 2015) and within Australia (Hamilton et al., 2018) for the adoption of a competency-based approach to
A national and global gap in transferrable work-ready skills among new graduates has led to calls both internationally (Strohmetz et al., 2015) and within Australia (Hamilton et al., 2018) for the adoption of a competency-based approach to curriculum design, focusing on skill acquisition as well as discipline knowledge (Jhangiani & Hardin, 2015). Despite this, undergraduate programs still often lack structured opportunities for students to develop generalisable skills that are highly valued by employers, such as resilience, critical thinking and reflective practice. Responding to this gap, the subject Applied Psychology (PSY1APP) was recently developed at La Trobe University to build work-ready competencies in psychology students. PSY1APP promotes deep active learning and strong student engagement through peer-to-peer knowledge construction, experiential learning (Kolb, 1984), and innovative digital tools. These include a unique reflective e-portfolio assessment, and video interviews with psychology alumni which are embedded into the Learning Management System and the e-portfolio, enhancing the authenticity of taught material and bridging the gap between psychology theory and practice. The current study aimed to examine the effectiveness of PSY1APP in increasing students’ generalisable skills and knowledge using a mixed-methods longitudinal study. Participants were 60 students (83.3% women, 15% men, 1.7% gender non-binary, Mage = 24.6 years, SDage = 9.86) who undertook PSY1APP in 2023. Students completed pre- and post-semester surveys measuring variables such as mindfulness, stress management and wellbeing. Qualitative open-ended text feedback was also solicited at post-test regarding students’ experience of the subject. Paired-sample t-tests revealed that upon completion of the subject, students practiced more mindfulness and stress management behaviours than at the beginning of semester; these effects were small to moderate in size. Students also reported improved attitudes and knowledge regarding reflective practices, stress management, behaviour change and teamwork; these effects were moderate to large in size. Thematic content analysis on open-ended feedback (Ryan & Bernard, 2003) revealed three main themes: students have improved 1) knowledge, 2) personal growth and 3) understanding of the connection of psychology to real life. Transformative change was driven by the use of digital tools such as the e-portfolio, enabling students to develop competencies such as reflective practice and project themselves into future professional roles. However, an increase in negative affect and a decrease in overall well-being were also observed, suggesting the need for additional support mechanisms. Our findings suggest that the deliberate integration of generalisable skills and innovative digital technologies into the undergraduate curriculum can meaningfully enhance students’ career-relevant capabilities, including mindfulness, stress management, reflective practice, behaviour change techniques, and teamwork. Given the limited opportunities for structured, career-focused skill development in undergraduate programs—particularly outside of placements and final-year subjects—PSY1APP represents a future-focused model. It offers a compelling example for educators in diverse disciplines seeking to embed professional readiness early in the student learning journey.
This Pecha Kucha presentation offers a reflective autoethnographic narrative of one third space educational developer’s transition from operational course-level support to co-leadership in the Scholarship of Teaching and Learning (SoTL)
This Pecha Kucha presentation offers a reflective autoethnographic narrative of one third space educational developer’s transition from operational course-level support to co-leadership in the Scholarship of Teaching and Learning (SoTL), curriculum transformation, and digital innovation. Working within a faculty-based initiative that integrates embedded scholarship with academic partnership, this role illustrates how hybrid professionals contribute to institutional transformation through relational, scholarly practice. The presentation is grounded in Third Space Theory (Whitchurch, 2008), which conceptualises the space between academic and professional roles as a site of emergent identity and leadership. Turner’s (1969) concept of liminality further describes the transitional nature of this work, situated within and across organisational boundaries. The approach is also informed by Hutchings, Huber, and Ciccone’s (2011) integrative vision of SoTL, which emphasises embedded scholarly practice as a catalyst for institutional change. Drawing on Felten’s (2013) principles of good SoTL practice and Lukes et al.’s (2024) strategic tools for building SoTL cultures, the presentation showcases the scholarly agency of third space professionals in enabling evidence-informed teaching and learning improvement. The twenty-slide narrative unfolds in four interconnected arcs. First, it traces the role evolution from reactive support to proactive scholarly partnership in SoTL. Second, it reflects on hybrid professional identity, including the emotional labour, invisibility, and pursuit of legitimacy often associated with third space roles. Third, it illustrates practice-based contributions to student learning through program-level assessment design, capability framework development, and collaborative SoTL projects. Fourth, it considers institutional enablers and constraints that influence the sustainability and visibility of third space contributions. Although focused on a single professional trajectory, the presentation contributes to broader sector insights. It foregrounds the methodological and strategic capacities third space professionals bring to academic development and SoTL. It argues that advancing educational transformation requires institutions to embrace more inclusive models of academic-professional partnership and to recognise hybrid roles as core to future-focused learning environments. Aligned with ASCILITE 2025 themes of Your Journey, Collaborative Futures, and AI and Human Synergy, this Pecha Kucha offers a practitioner-scholar perspective on educational innovation. It invites reflection on how third space professionals can be positioned as co-designers of pedagogical change, and how institutions can foster cultures where collaborative expertise is both valued and sustained.
