ASCILITE Publications (ISSN 2653-665X) provides a peer-reviewed fully open access publication platform for traditional and non-traditional publications in the field of Technology Enhanced Learning (TEL) in Australasia and abroad. It aims to provide a scholarly distribution and publication pathway for these alternative forms of best practice and thought to traditional journal articles - increasing the reach and impact of TEL to international contributions and an audience beyond the academy. ASCILITE Publications encourages contributions and involvement from early-career academics (including PhD and Masters candidates), teaching practitioners and professional staff.
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.
generative artificial intelligence (genAI)
Copyright (c) 2025 Anne-Marie Chase https://creativecommons.org/licenses/by/4.0
Feedback is a cornerstone of effective learning, yet it remains one of the most persistently complex challenges in higher education, for educators and students alike. This workshop introduces current thinking about student-centred feedback practices, and then explores the implications for how we can design valuable (and sustainable) feedback encounters across the course and through technologies. The workshop has four broad sections: Understanding feedback: This section briefly explains current thinking about feedback as a student-centred process. It introduces concepts of evaluative judgment, and feedback literacy. Building off these concepts we begin to identify a series of heuristics for our feedback designs. Designing for a feedback-rich course: Using research-informed design heuristics, educators and learning designers will work collaboratively to audit and reshape their own courses to support a feedback-rich experience, one that builds student agency, feeds forward, and fosters continuous engagement. Along the way we will tackle questions such as how we can build early and frequent feedback encounters, and explore examples of impactful exemplar strategies, AI formative feedback, and peer feedback. Sustainable feedback delivery: This section of the workshop narrows to the practice of teacher-delivery of feedback. The provision of feedback by teachers is a time consuming, effortful and emotional task. This workshop draws on a decade of research into audio, video, and screencast-based feedback and will offer explicit guidance on how to create these rich feedback artefacts, and how to do it efficiently. Through hands-on activities, participants will create their own multimedia feedback artefacts, testing sustainable workflows and content principles. Debating the role of AI in feedback: The workshop will close with a discussion of the emerging possibilities of AI feedback agents. While these rapidly evolving tools raise exciting possibilities for scalability and responsiveness, they also invite critical reflection. Participants will be introduced to some examples of AI-generated feedback practices and debate its strengths and limitations to those of human-mediated responses. By the end of this workshop, participants will have: Applied feedback theory to design a feedback-rich course tailored to their own teaching context; Produced audio, video, or screencast feedback artefacts testing sustainable workflows and research-based principles; Debated the potential and limitations of AI-generated feedback. This workshop is designed for educators, learning designers, and researchers seeking practical, research-informed, and critically reflective approaches to enhancing feedback practices through digital tools.
ASCILITE Conference - Pre-Conference Workshops
Copyright (c) 2025 Michael Henderson https://creativecommons.org/licenses/by/4.0
What if leadership in higher education wasn’t about position or power—but about purpose, connection, and collective impact? This workshop invites you to think differently about leadership. It offers space to reflect, write, and reimagine your leadership practice in a way that is relational, values-led, and grounded in community. Together, we will challenge dominant ideas—such as the lone hero, top-down command, or leadership as control—and instead explore approaches that centre listening, inclusion, and alignment across roles, disciplines, and initiatives. Designed for educators, researchers, professional staff, and academic leaders at all levels, the workshop creates a collegial and constructive space for deep thinking and real conversation. Participants will engage with structured frameworks to identify their leadership contexts, surface the challenges and opportunities they face, and explore what it means to bring people along to do hard things—together. Through guided writing, narrative development, and critically constructive peer coaching, this is a space for people who want to lead with clarity, kindness, and commitment to shared purpose.
Copyright (c) 2025 Kelly Matthews https://creativecommons.org/licenses/by/4.0
This workshop aims to: Introduce participants to a human-centred framework for evaluating educational technologies. Explore how this framework has been adapted to assess AI tools in teaching and learning. Critically evaluate the parameters of the AI evaluation framework- what’s missing, where do we need to go next- and contribute to collaborative input for cross-institutional development. Facilitate collaborative evaluation of AI tools across key dimensions: pedagogical value, ethical considerations, and impact on human expertise.
Copyright (c) 2025 Joan Sutherland, Nicholas English https://creativecommons.org/licenses/by/4.0
Following the COVID-19 pandemic, the emergence of generative AI has created significant upheaval for Australian tertiary education. This uncertainty has been exacerbated by policy uncertainty both globally and within the Australian context. In alignment with the conference theme, this workshop will explore what it means to educate in an era of continuous change. The workshop will adopt a format built on Klein's (1998) decision-making methodologies. There will be two phases: the first will focus on mapping future scenarios for Australian tertiary education, and the second will focus on what needs to occur now, particularly in terms of changes to practice. Participants will gain collective insight into future trajectories for tertiary education and develop practical approaches for adapting teaching and learning accordingly.
Copyright (c) 2025 Jason Lodge, Edward Palmer https://creativecommons.org/licenses/by/4.0
The sudden emergence and unprecedented growth of GenAI tools in the higher education landscape presents educators with new and significant challenges. In the face of these, we argue that educators should move from prohibition to supporting students to learn through collaboration with GenAI, but that their own values towards assessment and AI are central to this shift. This workshop will build on an assessment typology developed by the authors. The typology has been designed to assist educators to identify and communicate authorised GenAI use, and to build assessments that identify and prescribe appropriate student use of GenAI tools. Participants will leverage GenAI tools to reflect on their own biases and practices related to assessment design and GenAI. This reflective approach encourages educators to explicitly articulate aims for student learning as well as their own values for assessment design. The hands-on workshop will offer participants opportunities to deepen their teaching practice, while refining assessments relevant to their own teaching contexts. The design and delivery of the workshop will draw on the hosts’ experiences providing professional development workshops in their home institution that have introduced staff to GenAI tools, challenges and uses; teaching practice; assessment design; and student feedback literacy.
Copyright (c) 2025 Kate Tregloan, Huiseung (Sarah) Song, Shannon Rios https://creativecommons.org/licenses/by/4.0
The ongoing expansion of Generative Artificial Intelligence (GenAI) technologies since the launch of OpenAI’s ChatGPT in late 2022 has created an unprecedented level of uncertainty about learning and teaching practice in the tertiary education sector. The rapid pace of change and the steady release of new tools and functionalities have made staying informed a challenge for time-poor educators, students, institutional leaders and third space practitioners. This light-hearted panel discussion aims to alleviate some of this stress by showcasing the strengths and weaknesses of some of the leading GenAI platforms by comparing their performance when tasked with a selection of learning and teaching-related use cases. Our goal for the session is to provide attendees with an opportunity to discuss the important characteristics of useful GenAI applications as we test and develop an assessment rubric for the output from the bots and evaluate the tools together.
Copyright (c) 2025 Penny Wheeler, Colin Simpson, Wendy Taleo, Antony Tibbs https://creativecommons.org/licenses/by/4.0
Higher education faces declining student engagement, resulting in poor motivation and course completion rates. Students struggle with workload management, social disconnection, and limited meaningful interactions with instructors and peers (Naylor et al, 2017). These challenges are intensified in digital learning environments where isolation is common. Artificial Intelligence (AI)-driven nudging systems offer potential solutions through timely, personalised feedback to improve motivation, increase behavioural engagement, and reduce dropout rates by restoring connection and teacher presence. This panel examines tensions surrounding persuasive AI through the EngageAI project, which uses persuasive AI technology to deliver personalised behavioural nudges to enhance student’s learning experience. Four panellists will discuss: Does AI nudging's technical promise translate into meaningful outcomes? Are we empowering learners or creating algorithmic dependency? Can persuasive AI survive institutional constraints like budget limitations, privacy policies, and educational conservatism? The panel explores whether AI nudging represents adaptive learning's future or technological control over student behaviour.
Copyright (c) 2025 Rhodora Abadia, Gosia Ludwichowska-Alluigi, Reid Honan, Srecko Jocsimovic, Martin McCarron https://creativecommons.org/licenses/by/4.0
Rethinking assessment strategy in the age of Generative AI and considering programmatic design
Generative Artificial Intelligence (Gen-AI) presents a profound challenge for designing and implementing authentic tertiary assessment. The rapid adoption of platforms such as OpenAI’s ChatGPT has intensified concerns about assessment validity and integrity (Namoun et al., 2024). These concerns are particularly salient in health science (HS) programs, where excessive reliance on Gen-AI may undermine the development of clinical reasoning and decision-making competencies, affecting professional readiness and patient safety. This presentation reports on a project employing a three-cycle action research methodology to enhance responsiveness to Gen-AI–related assessment challenges. Cycle 1 involved developing an innovative Assessment Appraisal Tool aligned with national and institutional integrity guidelines (Flinders University, 2023; Lodge et al., 2023a, 2023b; Monash University, 2023; TEQSA, 2022; Torrens University, n.d.). The tool assigns an AI risk score (out of 30) indicating the likelihood that an assessment could be generated by Gen-AI and was piloted across three foundational HS subjects. Cycle 2 refined the tool based on feedback and extended its application to additional HS subjects, identifying targeted strategies for redesign. Cycle 3, currently underway, implements and evaluates these redesigns to improve authenticity, learning, and academic integrity. We will also discuss the emerging theoretical links between this project and programmatic assessment design. We will conclude by considering the strategic and practical meeting points where this project may align with future institution-wide programmatic assessment design strategies, addressing the ongoing challenges of Gen-AI while ensuring assessment of learning.
Copyright (c) 2025 Noosha Ehya, Amber Moore, Manisha Thakkar, Dhivya Rajasekaran https://creativecommons.org/licenses/by/4.0
This panel explores the lived experience of cross-institutional collaboration in a major multi-university research initiative investigating students’ perspectives on generative AI in higher education. Bringing together researchers from four leading Australian universities, the project exemplifies how sector-wide partnerships can overcome institutional silos, foster methodological innovation, and generate richer, more inclusive insights. Panellists will reflect on the benefits, challenges, and transformative potential of working collaboratively at scale, offering practical strategies and provocations for others seeking to harness collective wisdom in response to sector-wide challenges.
Copyright (c) 2025 Trish McCluskey, Michael Henderson, Kelly Matthews, Margaret Bearman, Lisa-Angelique Lim, Aneesha Bakharia https://creativecommons.org/licenses/by/4.0
The rapid rise of Generative AI (GenAI) tools is reshaping conversations about assessment and feedback in higher education. While much institutional attention focuses on detection, compliance, and academic integrity (Cotton et al., 2024), this presentation shifts the lens to educators and how they are actually using GenAI in assessment practice. We present findings from a grant-funded initiative at UNSW that explores educator-led innovation through a Postcards of Practice approach. The Postcards of Practice are one-page, practice-based narratives where educators document their use of GenAI tools. These postcards highlight applications including formative feedback generation, student prompting literacy, assessment redesign, and co-creation with AI. They reveal how educators are experimenting with GenAI to support student learning while navigating ethical concerns, transparency, and pedagogical alignment. Our study uses a qualitative interpretive methodology, combining thematic analysis of the postcards with follow-up interviews. The analysis draws on theoretical frameworks including feedback literacy (Carless & Boud, 2018), dialogic assessment (Nicol, 2010), and new paradigm feedback design (Winstone & Carless, 2020). We also apply institutional and national GenAI guidelines (Liu & Bridgeman, 2023; Perkins, 2023) to surface shared values such as authenticity, inclusivity, and responsible innovation that guide educators’ decisions. The aim of this study is to explore how educators are experimenting with GenAI in assessment and feedback, and to capture their emerging practices and reflections through the Postcards of Practice initiative. The central research question guiding this work is: How are educators integrating GenAI into assessment and feedback, and what opportunities, challenges, and support needs arise from these practices? This work advances Technology Enhanced Learning (TEL) by providing empirical insights into how GenAI is actually integrated at the coalface of teaching. Educators describe how GenAI supports more frequent, personalised feedback and builds student agency in learning. At the same time, they raise concerns about over-reliance, AI hallucination, and the need for clear pedagogical scaffolding. These reflections point to the need for professional development that is discipline-sensitive, responsive, and grounded in practice. The postcard approach also functions as a professional learning intervention. It prompts reflection, encourages cross-disciplinary dialogue, and helps build a local community of practice around GenAI use. Through this model, we demonstrate an innovative and scalable method of capturing and supporting TEL innovation in real time. The findings suggest GenAI is prompting a rethinking of assessment: from summative, compliance-driven models to more transparent, formative, and student-centred designs. Educators begin to embed feedback literacy, ethical AI use, and critical prompting into their teaching, with clear implications for program-level assessment and graduate capability development. To strengthen clarity, we propose a concise diagram mapping the emerging practices captured in the postcards against the theoretical frameworks of feedback literacy, dialogic assessment, and new paradigm feedback design. This visual representation illustrates how practical insights align with, extend, or challenge these frameworks, making the study’s contribution accessible across diverse tertiary contexts. This proposal offers exemplary innovation in TEL by foregrounding bottom-up, practice-led experimentation with GenAI. It is grounded in strong theoretical frameworks and applicable across diverse tertiary contexts. The Pecha Kucha format will present key insights through rich visual storytelling, including excerpts from the postcards themselves. We conclude by proposing future directions for research and institutional strategy, including how to embed GenAI into assessment ecosystems in ways that enhance learning, uphold integrity, and empower educators to lead digital transformation from within.
Copyright (c) 2025 Michael Cowling, George Joukhadar, Pranit Anand, Siobhan Wills, Kate Nicholls https://creativecommons.org/licenses/by/4.0
Professional standards for learning design
Learning design as a profession remains in a liminal space, increasingly central to digital education yet without the formal recognition or collective voice enjoyed by other professional domains. This panel addresses the current lack of shared professional standards by asking: What values, practices and indicators of quality might form the basis of a professional standards framework for learning designers? Drawing on established models from education and technology professions, and sociological theories of professionalisation, the panel invites ASCILITE participants to collaboratively explore what such a framework might look like, how it should be developed, and why it matters. Through guided provocations and participatory workshop activities, attendees will help shape a draft map of core values, domains of practice and levels of expertise, forming the foundation for further community-led work.
Copyright (c) 2025 Michael Henderson, Keith Heggart, Nhung Nguyen, Simon Paxton, Jacqueline Trebilco, Scott Bradey, Lila Azouz https://creativecommons.org/licenses/by/4.0
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.
Copyright (c) 2025 Diana Saragi Turnip https://creativecommons.org/licenses/by/4.0
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.
Copyright (c) 2025 Katja Petrovic, Carina Chan https://creativecommons.org/licenses/by/4.0
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.
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.
Copyright (c) 2025 Charmaine Herfkens-Fernandez https://creativecommons.org/licenses/by/4.0
Collaborative reflection in online education
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.
Copyright (c) 2025 Rupinderdeep Kaur, Amanda Bridgewater, Joanne Harmon https://creativecommons.org/licenses/by/4.0
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.
Copyright (c) 2025 Jeremy Stothers, Yogita Ahuja, Prerna Varma https://creativecommons.org/licenses/by/4.0
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.
Copyright (c) 2025 Md Haseen Akhtar, Thomas Cochrane https://creativecommons.org/licenses/by/4.0
Course-aligned generative AI-based intelligent tutoring
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.
Copyright (c) 2025 Abdullahi Chowdhury, Siamak Mirzaei, Mohammad Afikuzzaman https://creativecommons.org/licenses/by/4.0
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.
Copyright (c) 2025 Tanya Henry, Dale Hansen https://creativecommons.org/licenses/by/4.0
The enduring value of teachers in feedback processes
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.
Copyright (c) 2025 Jimena de Mello Heredia, Michael Henderson, Margaret Bearman, Jennifer Chung, Tim Fawns, Simon Buckingham Shum, Kelly Matthews https://creativecommons.org/licenses/by/4.0
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.
Copyright (c) 2025 Jennifer Boreland, Tanya Henry, Sue Sharpe https://creativecommons.org/licenses/by/4.0
Rewiring EdTech support: A collaborative model for empowering staff capability at scale
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.
Copyright (c) 2025 Anoop Pappachan Malekudiyil, Pradhiban Duraisamy https://creativecommons.org/licenses/by/4.0
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.
Copyright (c) 2025 Stoo Sepp https://creativecommons.org/licenses/by/4.0
Developing a values-based ethics learning tool
Disruptive technologies create value tensions that students need to consider, irrespective of their discipline or professional context. These value tensions arise when competing values are present in a technosocial context. In 2018-2019, a card-based ethics learning tool was created to support students in developing values-based ethics and data literacy competencies. The cards engage students in considering ethical dilemmas and values when designing or using technologies and are informed by doctoral findings and classroom observations (Fernando, 2017; Fernando, 2020). The first prototype of these cards was primarily aimed at undergraduate IT students, and was presented to local collegiate communities, at MozFest 2019, a conference sponsored by Mozilla, and at ETHICOMP 2020, the leading international computer ethics conference (Author, 2020). This early prototype of the conversation cards was based on value tensions but had not been validated or expanded to address the values-based ethics competencies in other discipline contexts. At present, the authors are working on a funded Scholarship of Teaching and Learning (SoTL) project to extend the initial prototype by applying the theorising of Shannon Vallor (2018) and Shoshana Zuboff (2019) to the value tensions conceptual framing. Vallor (2018) theorised 12 technomoral virtues that people should consider when using or designing technologies, whereas Zuboff (2019) theorised surveillance capitalism, where the commodification of personal data through the design and use of technologies may curtail human freedom and dignity. The technomoral virtues and surveillance capitalism informed our identification of values to include in the conversation cards. These theoretical framings provide rigour while establishing clarity for learners. Student-centred and active learning pedagogical principles are realised in the conversation cards (Dada et al., 2023). The cards support problem-solving, reasoning, ethical understanding and responsible decision-making, while allowing for flexible, self-paced, learning in pairs. A PRISMA scoping survey revealed that such cards are an innovative contribution to the field, and at present we are validating the cards through a repeated-measures study followed by focus group discussions. The repeated-measures study uses a factor vignette survey eliciting responses on a 7-point Likert scale eliciting quantitative data to be analysed with an Ordinal Logistic Regression, to reveal which combinations of social and market values are the most readily identifiable as problematic. The focus groups will explore participants’ thoughts and responses about the cards to elicit qualitative data to contribute nuance and clarity to the quantitative data. To support future educators, this project will enhance educational accessibility by developing an Open Educational Resource (OER). The effective implementation of the OER hinges on User Experience (UX) and usability principles and its development will be guided by Jacob Nielson’s 10 Usability Heuristics (1994) and Norman’s broader design principles (2013). The OER interface will incorporate clear navigation and intuitive interaction to guide learners through complex ethical reasoning while being highly usable (Mayer, 2014) and will provide a feedback-rich environment. The OER will be platform independent, support diverse disciplines and educational approaches (Bower, 2010). Adopting the Web Content Accessibility Guidelines (WCAG) principles (W3C, 2023) will ensure universally accessibility, while visual design principles (Williams, 2014) will immerse learners and facilitate understanding of complex ethical dilemmas.
Copyright (c) 2025 Anisha Fernando, Siaw Mei Sim, Kathy Darzanos, Kirsten Wahlstrom, Nina Evans https://creativecommons.org/licenses/by/4.0
Seeking a foundational understanding of instructional video use in tertiary music education
Despite the widespread use of instructional video and other technologies across higher education, the role of learning technologies in tertiary music education remains largely unexplored (Zhukov, 2015). Instructional videos have been shown to be a widely used pedagogical tool within higher education in Australia (Colasante, 2022; Fyfield et al., 2019; Henderson et al., 2017). Music is a domain in which students typically engage in extensive independent practice and learning (Jorgensen, 2000; Zhukov, 2015), presenting a strong use case for the provision and use of asynchronous learning materials to support this independent study and practice (Grant, 2013; Johnson, 2022). However, in the context of tertiary music education, there is little empirical evidence to confirm widespread use of instructional videos in explicit or self-directed learning, or to explore the pedagogical purposes they serve (DeWitt et al., 2013). This Pecha Kucha reports on preliminary findings from the first phase of a mixed-methods research project on the use of instructional videos in tertiary music education. The research project aims to address these gaps in the literature by (1) establishing foundational understandings of current practices relating to instructional video in tertiary music education, (2) investigating how educators’ values and perspectives influence pedagogical decisions regarding video selection and design, and (3) evaluating the impact of various multimedia design features on student learning gains, drawing on theories such as Mayer’s (2014) Cognitive Theory of Multimedia Learning (CTML) for analysis in subsequent phases. The first phase of the research involved distributing an online survey to tertiary music educators and students across Australian and international institutions. The survey collected both quantitative and qualitative data, which are currently being analysed using statistical methods and inductive thematic analysis, respectively. Quantitative data describes the extent, frequency, context, and purpose of instructional video use in tertiary music teaching, and qualitative data captures the reasoning for utilising instructional videos, factors influencing adoption, and perceived pedagogical quality. The online survey was conducted in the second half of 2025. While data collection and analysis are ongoing, this Pecha Kucha presents a descriptive analysis of the data collected to date, providing an initial picture of how instructional video is currently being used (or not used) in tertiary music education contexts, as well as educators’ and students’ experiences with and attitudes toward the use of instructional video within the tertiary music context. This research aims to make a novel contribution to the fields of music education and digital pedagogy by providing a foundational view of the use of instructional video in tertiary music education, with a focus on the Australian context. It will also provide insight into educators’ pedagogical reasoning and preferences, offering evidence to inform the design of more effective, discipline-specific video resources. In doing so, the research aims to support more pedagogically grounded use of instructional video within the tertiary music context. More broadly, instructional video can enhance accessibility and inclusion by supporting regional learners, enabling flexible engagement with diverse expertise, and fostering equitable participation in independent learning.
Copyright (c) 2025 Joshua Milton https://creativecommons.org/licenses/by/4.0
PolyFeed—Enhancing feedback literacy and strategies with feedback analytics
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.
Copyright (c) 2025 Yi-shan Tsai, Bhagya Maheshi, Hiruni Palihena, Thomas Ka Ho Ng https://creativecommons.org/licenses/by/4.0
Enhancing engagement in large postgraduate capstone classes
Student engagement is increasingly recognised as an important factor influencing achievement and learning in higher education (Kahu & Nelson, 2018; Mandernach, 2015). Sustaining meaningful engagement in large classrooms is difficult, particularly with diverse student cohorts and generational differences in learning preferences (Cuseo, 2007; Giunta, 2017; Maringe & Sina, 2014; Mendoza, 2019). While technology-enhanced learning (TEL) tools are widely used to promote engagement through active learning (McKinsey & Company, 2022), they do not always address the deeper, relational aspects of engagement that help students feel connected and supported (Crawford et al., 2024). In an era of constant technological change, personal teacher-student and peer relationships remain vital to creating safe and supportive environments where students feel seen and are encouraged to share ideas, ask questions, and seek help. This Pecha Kucha introduces a classroom-based intervention designed to enhance engagement in a large postgraduate accounting capstone unit. It draws on Kahu and Nelson’s (2018) concept of the educational interface, a psychosocial space where institutional and student factors interact. It also builds on the findings of Kahu, Picton and Nelson (2020), who found that supportive conversations, empathy and encouragement as key triggers for student engagement. This intervention involves an oral participation assessment that incorporates structured participation and one-on-one interaction to foster connection and conversation. The unit enrolled approximately 250 students across three workshops of 65–95 students each, many from diverse cultural and linguistic backgrounds. Engagement in such contexts is challenging, particularly when students face barriers to oral participation. To address this, the participation assessment was reimagined to be inclusive and multifaceted. For consistency and efficiency in assessment, groups were formed through a structured networking activity in Week 1, establishing table-based peer cohorts for the semester. Participation was scaffolded through three pathways: whole-class discussion, small-group conversation, and one-on-one interaction with staff. During case-based activities, staff and assistants rotated between tables in structured engagement rounds. These real-time conversations provided quieter or linguistically hesitant students with opportunities to contribute in low-pressure settings. Weekly feedback was integrated into class time to reinforce progress and support iterative learning. Although digital tools and generative AI were embedded to aid discussion, content review, and peer collaboration, the heart of the practice lies in the high-touch, relational nature of teaching. It highlights how innovation in TEL can come from reimagining how we use structure, space, and time within existing classroom ecosystems. This approach complements technology in education by embedding purposeful human interaction that strengthens engagement and learning. Students reported high levels of satisfaction, particularly with having multiple ways to participate and opportunities to feel seen and heard. One staff member reflected that the structured conversations mirrored workplace readiness by giving students practice in reporting succinctly to senior leaders. Workshop assistants noted that the assessment motivated preparation and improved real-time communication, particularly for students less inclined to speak voluntarily. Challenges included time pressures associated with rotating through tables and ensuring fairness in marking. These were managed through collaborative planning, transparent criteria, and clearly defined participation roles. The overall outcome was a relational learning environment that made large classes feel smaller, more inclusive, and more responsive. In an era of digital transformation, this case highlights the enduring value of intentional, human-centric assessment design. Purposefully combining TEL with low-tech strategies affirms that low-tech does not mean low-impact; rather, it demonstrates pedagogical resilience that places equity, connection, and student voice at the centre of technology-rich higher education.
Copyright (c) 2025 Dhayani Kirubaharan https://creativecommons.org/licenses/by/4.0
Empowering higher education through heutagogical assessment strategies
This abstract explores how the redevelopment of a Master of Education program embraced heutagogical principles to create an adaptable, resilient, and future-ready learning environment. Grounded in adult learning theory and social constructivism (Blaschke, 2019), heutagogy offers a compelling response to the needs of contemporary students navigating diverse professional, cultural, and technological contexts. Our subject redesign began with a foundational question: Who are our learners? In our case, they are working professionals with rich, varied backgrounds who seek meaningful opportunities to apply and extend their expertise. Traditional assessment strategies often constrain such learners, focusing on prescriptive outputs rather than individual growth and agency. In contrast, a heutagogical approach empowers students to actively shape their learning journeys by setting personal goals, selecting relevant resources, and reflecting on real-world application (Blaschke & Hase, 2015). This session shares how we embedded heutagogical principles into every layer of the curriculum, particularly the assessment strategy. The assessment strategy intentionally incorporates assessment of, for, and as learning, recognising the importance of evaluation, feedback, and learner self-regulation as interconnected components of the learning process. Metacognition and self-efficacy are intentionally scaffolded to support learners in becoming resilient and reflective. Assessments are not merely tools of measurement but engines of learning. From reflective journals to digital toolkits and advocacy plans, students are encouraged to explore their own context and present their understanding in diverse formats. The emphasis shifts from product to process, promoting engagement, academic integrity, and deeper conceptual learning (Blaschke & Hase, 2015). A key feature of the assessment design is the focus on real-world relevance and personal meaning. Students are encouraged to explore issues or challenges connected to their own professional or community context, applying theory to practice in ways that create tangible value. This approach embodies authentic assessment, situates learning in lived experience, and reflects broader course themes such as empowerment, advocacy, and social responsibility. As a result, students often report a stronger sense of ownership over their work, and educators observe greater depth, originality, and critical engagement in their submissions. The reflective structure of the assessments also supports social-emotional learning by encouraging learners to explore their identity, values, and professional purpose, fostering wellbeing alongside skill development. The heutagogical model also fosters inclusivity and adaptability. Offering students agency in how they demonstrate mastery not only accommodates diverse learning needs but encourages critical and creative thinking. Through strategies like process-driven assessment, learner-contextualised tasks, and reflection-infused outputs, students are supported to become agile, self-regulated learners prepared for an uncertain future (Carless et al., 2017). The design is sustainable across hybrid, online, and face-to-face delivery modes, ensuring consistency of student experience and adaptability to institutional needs. Incorporating Artificial Intelligence into Authentic Assessment As artificial intelligence reshapes the educational landscape, its integration into assessment design must be intentional and values driven. In our Master of Education redevelopment, AI is positioned as a catalyst for deeper inquiry. Students are encouraged to critically engage with AI tools, such as generative platforms, to support research, ideation, and reflection. This approach aligns with heutagogical principles by fostering learner agency, self-direction, and metacognitive awareness (Blaschke & Hase, 2015; Narayan & Vanderburg, 2025). Rather than policing AI use, assessments are designed to promote transparency, ethical reasoning, and critical evaluation of AI-generated content. Learners reflect on how AI influences their thinking, decision-making, and professional practice, embedding academic integrity within a framework of trust and personalisation (Anthology, 2024; Dolbin et al., 2024). This empowers students to become discerning users of technology, capable of navigating complex, real-world challenges with confidence and integrity. AI-supported assessment also scaffolds metacognition and self-efficacy, enabling learners to identify patterns in their thinking and refine their approaches (Lineman, Sweet, & Sutton, 2025; Searle Center, 2025). These strategies align with process-driven, authentic assessment models that prioritise learner context, critical engagement, and adaptability (Carless et al., 2017). Looking ahead, we argue that assessment design must prioritise flexibility, authenticity, and student voice. In the age of artificial intelligence and rapid technological transformation, academic integrity cannot rest on control or surveillance. Instead, it must be built on trust, personalisation, and critical engagement. Heutagogy offers a timely and sustainable path forward, where learners are not passive recipients of content but co-creators of their educational experiences. This presentation offers practical strategies, examples, and design principles that participants can adopt and adapt in their own contexts. In doing so, it contributes to the conversation on how higher education can remain relevant and empowering. It aims to equip learners to thrive in a future defined by continuous change.
Copyright (c) 2025 Ashley Howard Kerr https://creativecommons.org/licenses/by/4.0