Topic outline
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The rise of artificial intelligence tools has prompted a revaluation of assessment practices, here are some innovative approaches (papers and presentations) being implemented:
UniSA
Papers and Websites
- The TIU-produced and maintained guide to Artificial Intelligence for Teaching and Learning in Higher Education is an excellent place to start for information on artificial intelligence basics as well as teaching with generative artificial intelligence, assessment design considerations, and the broader issues of bias and copyright. The section on AI and Assessment Design includes information and advice on developing acceptable use statements for assessments.
- Information on Assessment Policy and Procedure and Artificial Intelligence and tools that students and staff can use are available on the GenAI - Teaching Innovation Unit - Intranet - University of South Australia
- Survey participant Guidelines
Presentations
- HERGA Conference - Responsible Generative AI Integration in HE - Oct 2025 (PDF)
- GenAI Panel Discussion - Teaching and Learning Forum for Deans of Programs and Program Directors - May 2024 (PDF)
- AAIN Academic Integrity Integrity Forum - Sept 2025 (PDF)
Watch (21m 30s)
Towards Procedural Fairness in GenAI-related Academic Misconduct Investigations -
Academic Integrity Officers Call for Educative approachesPresenters:
Dr Abdullatif Lacina Diaby, Dr El-Sayed Abd-Elaal, Amanda Janssen, Dr M. Shokry Abdelaal and Elizabeth Smith, University of South Australia
Abstract:This study presents findings from a survey of Academic Integrity Officers (AIOs) conducted 18 months after ChatGPT's release. It examines how AIOs at the University of South Australia perceive and internalise key elements of procedural fairness when addressing academic misconduct involving generative artificial intelligence (GenAI). Despite 90% of AIOs investigating GenAI-related cases, only 21% felt technical confident doing so, creating significant challenges to consistent and fair misconduct responses. The study also shows notable disparities in GenAI competency among AIOs, with 63% reporting intermediate skills, while 26% identify as novices. These gaps risk undermining transparency and consistency, key pillars of procedural fairness. In response, AIOs advocate for educative over punitive approaches to mitigating GenAI misuse and promoting academic integrity. They strongly prefer assessment design modifications (88.2%) and ethical discussions with students (76.5%) over relying primarily on detection tools (39.5%). These preferences reflect a commitment to restorative justice, emphasising students learning and growth. The findings highlight an urgent need for standardised GenAI training protocols for AIOs and underscore the vital role of academic staff in promoting procedural fairness. Effective responses to GenAI misuse must integrate transparent policies, consistent investigative procedures, and educative interventions that uphold academic integrity while helping students navigate ethical technology use.
The University of Sydney
Using Danny Liu’s ‘two lane approach’, this method examines the purpose of assessment - whether it is of, for or as learning - and then determines the appropriate level of artificial intelligence tool usage and what security measures need to be adopted. For more information and examples refer to:
The new Sydney Assessment Framework (the two-lane approach): AI for Educators
The University of Adelaide
Based on the Artificial Intelligence Assessment Scale (Perkins, Furze, 2024) this approach provides guidelines on communicating expectations for generative AI use in assessment:
The University of Melbourne
Offers resources, information guidance and case studies on good assessment practices that can enhance student learning and reduce academic misconduct:
Designing assessment tasks that are less vulnerable to AI
References:
Perkins, M, Furze, L, Roe, J & MacVaugh, J. The Artificial Intelligence Assessment Scale (AIAS): A Framework for Ethical Integration of Generative AI in Educational Assessment. (2024). Journal of University Teaching and Learning Practice, 21(06). https://doi.org/10.53761/q3azde36
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