Abstracts

Keynote presentation

re(?)thinking assessment feedback

Professor Michael Henderson, Professor of Digital Futures, and Director of Educational Design and Innovation in the Faculty of Education at Monash University, Australia

Assessment feedback is one of the most impactful processes in learning, yet it continues to present significant challenges for institutions, educators and students. In light of these challenges, it is not surprising that many people are turning to Generative AI as a potential solution. Certainly, there are fascinating and valuable contributions that can result from our entanglements with Generative AI. Studies are already revealing how students are independently seeking feedback from Generative AI, and how educators are leveraging the syntactic mimicry of Generative AI to enhance feedback encounters. However, not all of these applications are beneficial or address the underlying concerns of feedback. With this in mind it is timely to ask questions about the nature of feedback, what makes for effective feedback, and to consider where Generative AI may fit.

In this keynote, we will revisit notions of feedback as input and process, and explore deeper dimensions of its pedagogical and relational roles. The session invites participants to rethink feedback not merely as a transactional exchange but as a nuanced, dialogic and future focused interaction central to fostering genuine evaluative judgment and learner autonomy. With this in mind I will celebrate some applications of AI in supporting feedback processes, but I also adopt a cautious stance of its role, and somewhat hidden consequences.

Reimagining Feedback and Assessment Practices

  • Dr Jessica Lees (Presenting Author), Faculty of Medicine, Dentistry and Health Sciences & Ashley Anderson, Student and Scholarly Services

    This presentation provocatively reimagines rubrics not as evaluative tools, but as invitations for learning. Drawing on Universal Design for Learning (UDL) and feedback literacy, it introduces feed forward rubrics—a low-barrier, high-impact strategy that helps students identify next steps and supports programmatic assessment. Rather than highlighting deficits, these rubrics prompt reflection, self-direction, and growth. The approach aligns with ASE priorities, particularly inclusivity, innovation, and thriving student communities. Participants will engage with reflective prompts and co-design opportunities to consider how rubric language and structure can be reimagined to foster equity, agency, and sustainable learning across diverse cohorts.

  • Morag Burnie, Madeleine Young (Presenting Author), Student and Scholarly Services

    ​​We showcase the outcomes of a resource development project led by Academic Skills in collaboration with volunteer student partners and Video Media. The project developed a suite of multimedia and web based self-access materials to support development of student feedback literacy. ​

  • Assaf Dekel (Presenting Author), Dr Miriam Edwards & A/Prof Sean Pinder, Faculty of Business and Economics

    This study examines a self-assessment and peer feedback initiative within a large-cohort first year finance subject. The intention was to foster exam preparedness while also building community. Using FeedbackFruits©, each student solved a question set indicative of the exam, then using a given rubric, they self-assessed and anonymously reviewed a peer’s attempt. This was repeated four times throughout the semester. Subsequently, a mixed-methods study indicated that student participation within these activities had a positive effect on learning outcomes even after controlling for learner aptitude. This study illustrates the impact such a strategy may have on student engagement and motivation.

AI Integration in Assessment and Feedback

  • Dr Lili Chen (Presenting Author), Dr Winn Chow (Presenting Author) & Dr Stella Peng, Faculty of Engineering and Information Technology

    This study aims to address the longstanding challenges of grading in engineering mathematics which constrain markers’ ability to provide formative feedback. It explores a criterion-referenced grading approach supported by GenAI. The well-defined criteria focus on students’ overall understanding and therefore enable structured and formative feedback. The research adopts a mixed-methods approach to evaluate GenAI-assisted criterion-referenced grading in an undergraduate Electrical Engineering subject. Initial results show significant improvements in grading time, accuracy, and feedback. The study contributes to both literature and practice by proposing a replicable model for incorporating GenAI into STEM assessment workflows to improve feedback, efficiency, and assessment quality.

  • Dr Christopher Woods (Presenting Author), Dr Sharon Shan & Dr Jason Brown, Faculty of Business and Economics

    This session introduces a practical, time-saving method for producing high-quality feedback on reflective writing using Spark AI. Designed to ease the burden of marking large volumes of student work, the tool turns rough assessor notes into clear, actionable feedback and rubric-aligned suggestions for improvement. We’ll walk through how the method works, the initial prompt and subsequent development, showing examples from an undergraduate BCom internship subject. Participants will try building their own version of the AI prompt using a rubric from their teaching context, leaving the session with a draft that they can trial immediately in their own marking.

  • Angela Sojan (Presenting Author), Dr Winn Chow (Presenting Author) & Dr Stella Peng, School of Computing and Information Systems, Faculty of Engineering and Information Technology

    The rise of generative AI has disrupted traditional assessment methods in higher education, prompting universities to develop AI assessment guidelines. This study examined the guidelines from five major Victorian universities, analysing their alignment with formative feedback principles and their effectiveness in preventing AI misuse. Through a review of the guidelines and a student survey, this research evaluated ten common assessment approaches. The findings revealed that no single method is universally effective; each has distinct strengths and weaknesses. Based on these insights, the study proposed an AI assessment framework to support educators redesign assessments, enhancing formative feedback while mitigating AI misuse.

Authenticity and Adaptation in the AI Era

  • Dr Wasana Karunarathne (Presenting Author), Faculty of Business and Economics, Ashley Hanson (Presenting Author) & Chris Selman

    This presentation explores whether authentic assessment remains valid in an era of generative AI, using insights from a large first-year business statistics assignment completed by over 1,500 students. The assessment was designed to develop data analysis skills, analytical and creative thinking, written communication, and collaborative learning, all essential for business graduates, while maintaining accountability. Students were required to submit supporting files that demonstrated their process and reasoning, ensuring transparency and verifiability. Regular check-ins and structured group work supported engagement throughout the process. The study offers practical strategies for designing assessments that promote genuine learning and integrity in AI-enabled environments.

  • Ben Lawless, Faculty of Education

    This presentation explores how ChatGPT can be used to generate developmental assessment rubrics for K–12 and tertiary tasks. It introduces a prompt-engineered workflow and shares practitioner insights from school and university contexts. The session will demonstrate how educators can rapidly co-create rubrics that align with learning progressions, improve feedback, and support inclusive design.

  • Dr Nashid Nigar, Faculty of Education

    This presentation reconceptualises feedback as a relational, affective, and onto-epistemological encounter shaped by AI, hyper-diversity, and postcolonial complexity. Drawing on hermeneutic phenomenology and the Hybrid Professional Becoming approach, it explores feedback through lived vignettes—highlighting affective labour, translanguaging, and ethical tensions. Interactive methods include a Padlet memory wall, affective mapping, and a micro-toolkit for interculturally responsive feedback. Aligned with the University of Melbourne’s ASE Strategy, this self-study affirms feedback as situated, relational praxis grounded in care, complexity, and onto-epistemic justice.

Featured speakers