Generative Artificial Intelligence (GenAI) has caused a shift in approaches to assessment and academic integrity in tertiary institutions. Recent research internationally and within Australasia underscores the need for responsible GenAI conduct and preparation for a future where work and education are shaped by GenAI technologies. However, ethical considerations often need to be made based on the impact on human research, intellectual property rights, and AI literacy in higher education. Academic integrity and ethical considerations should be used to balance hasty approaches to GenAI to ensure tertiary institutions provide inclusive learning opportunities. All of these impact on the need to understand what motivates student use of GenAI. Using the framework proposed by Bouteraa et al. (2024) as a model, this presentation applies data and results from recent literature to explore the factors which motivate use of Generative AI in a tertiary education context. These factors consist of Performance Expectancy, Effort Expectancy, Technological Self-Efficacy, Educational Self-Efficacy, Integrity, and Personal Anxiety. The presentation highlights what factors make students likely to use Generative AI, what factors demotivate use, and how motivation and demotivation do not always produce a desired outcome.

In my role as Lead for Academic Integrity, I am frequently asked by staff about how they can catch students cheating using generative AI. I use an example of a paragraph that I have paraphrased, alongside two other examples where I have used ChatGPT and Quillbot to make the paragraph more academic and the original text, to help open the discussion about what they think is appropriate use. As part of the activity, we create a cline of acceptability where they consider what is and what is not acceptable. It is often also a useful introduction to them to understand what generative AI can do as well as learning how to understand what would be acceptable for them professionally, what is suitable for neurodiverse students, what is acceptable for speeding up, e.g., literature review searches, etc. This activity helps staff to have less of a negative or kneejerk reaction to students using generative AI and make more informed decisions about when generative AI contributes to good academic practice or poor academic practice. The poster will show the four paragraphs, and an example cline that we produced in a Help Navigating Academic Misconduct staff training workshop.

The Early Offer prioritises student wellbeing, efficiency, and incentivises honesty and learning. Students who accept an Early Offer choose their preferred educative approach: in-person integrity counselling or an asynchronous online module. Our response webform allows students to accept or decline at their convenience, without worrying about how to frame their response.
Three modules target key breach types: plagiarism, collusion, contract cheating and inappropriate use of artificial intelligence. The learning activities encourage self-reflection and good academic practice, including opportunities to practice skills like referencing. Communication is framed positively to build confidence and encourage self-belief and intrinsic motivation to navigate University successfully while maintaining academic integrity.
With 12 months of data now available, we can evaluate the uptake and effectiveness of the approach. In this poster, we share key insights from our experience to support the wider adoption of effective practices across the sector. Based on its early success, the new Adelaide University has adopted an Early Resolution procedure.

Students in higher education are increasingly turning to Generative AI (GenAI) to assist with their assessments—both in legitimate and improper ways. This growing reliance on GenAI has significantly reshaped the academic integrity landscape, shifting the focus of academic integrity teams toward identifying potential misuse. A common consequence of such misuse is the presence of fabricated (hallucinated) or misrepresented references, stemming from GenAI’s limitations in generating accurate and verifiable information.
Manually verifying these references by reviewing original source materials is time-consuming and resource-intensive, often resulting in investigation delays and a rise in student complaints.
This poster introduces two innovative methodologies that leverage GenAI itself to detect reference-related misuse more efficiently. Specifically, I will present tailored prompt engineering techniques designed to identify both hallucinated and misrepresented citations. These approaches provide a practical framework for streamlining academic integrity investigations by harnessing GenAI’s advanced text analysis capabilities.

Global interest in AI-supported assessment is intensifying, yet limited research examines alignment with regulatory expectations, particularly within Australian ICT education. This study addresses that gap through a national analysis of 917 publicly available documents from 39 Australian universities offering ICT programs. It explores how institutions are integrating GenAI in assessment practices while aligning with TEQSA’s core principles: preparing students for ethical engagement in AI-driven contexts and ensuring trustworthy assessments of competencies.
The study investigates three key research questions:

• RQ1. How do institutional policies in Australian universities align with TEQSA’s principles for AI-supported assessments?
• RQ2. What strategies, as documented in publicly accessible sources, are used to ensure fairness, transparency, and academic integrity in AI-driven assessments in Australian higher education?
• RQ3. What gaps or inconsistencies exist in institutional policies concerning ethical AI usage in ICT assessment practices?

This research is highly relevant to the academic integrity community as it interrogates how policy and practice intersect to uphold integrity in a rapidly evolving technological landscape. Findings highlight progress in policies, training, and AI literacy, but also expose inconsistencies in ICT-specific guidance. Recommendations aim to strengthen institutional readiness and enhance integrity through clearer policies and professional development aligned with TEQSA standards.