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Objective: To support a diverse sample of Australians to make recommendations about the use of artificial intelligence (AI) technology in health care., Study Design: Citizens' jury, deliberating the question: "Under which circumstances, if any, should artificial intelligence be used in Australian health systems to detect or diagnose disease?", Setting, Participants: Thirty Australian adults recruited by Sortition Foundation using random invitation and stratified selection to reflect population proportions by gender, age, ancestry, highest level of education, and residential location (state/territory; urban, regional, rural). The jury process took 18 days (16 March - 2 April 2023): fifteen days online and three days face-to-face in Sydney, where the jurors, both in small groups and together, were informed about and discussed the question, and developed recommendations with reasons. Jurors received extensive information: a printed handbook, online documents, and recorded presentations by four expert speakers. Jurors asked questions and received answers from the experts during the online period of the process, and during the first day of the face-to-face meeting., Main Outcome Measures: Jury recommendations, with reasons., Results: The jurors recommended an overarching, independently governed charter and framework for health care AI. The other nine recommendation categories concerned balancing benefits and harms; fairness and bias; patients' rights and choices; clinical governance and training; technical governance and standards; data governance and use; open source software; AI evaluation and assessment; and education and communication., Conclusions: The deliberative process supported a nationally representative sample of citizens to construct recommendations about how AI in health care should be developed, used, and governed. Recommendations derived using such methods could guide clinicians, policy makers, AI researchers and developers, and health service users to develop approaches that ensure trustworthy and responsible use of this technology., (© 2024 The Authors. Medical Journal of Australia published by John Wiley & Sons Australia, Ltd on behalf of AMPCo Pty Ltd.)

Background/objectives: Artificial intelligence (AI) holds remarkable potential to improve care delivery in dermatology. End users (health professionals and general public) of AI-based Software as Medical Devices (SaMD) require relevant labelling information to ensure that these devices can be used appropriately. Currently, there are no clear minimum labelling requirements for dermatology AI-based SaMDs., Methods: Common labelling recommendations for AI-based SaMD identified in a recent literature review were evaluated by an Australian expert panel in digital health and dermatology via a modified Delphi consensus process. A nine-point Likert scale was used to indicate importance of 10 items, and voting was conducted to determine the specific characteristics to include for some items. Consensus was achieved when more than 75% of the experts agreed that inclusion of information was necessary., Results: There was robust consensus supporting inclusion of all proposed items as minimum labelling requirements; indication for use, intended user, training and test data sets, algorithm design, image processing techniques, clinical validation, performance metrics, limitations, updates and adverse events. Nearly all suggested characteristics of the labelling items received endorsement, except for some characteristics related to performance metrics. Moreover, there was consensus that uniform labelling criteria should apply across all AI categories and risk classes set out by the Therapeutic Goods Administration., Conclusions: This study provides critical evidence for setting labelling standards by the Therapeutic Goods Administration to safeguard patients, health professionals, consumers, industry, and regulatory bodies from AI-based dermatology SaMDs that do not currently provide adequate information about how they were developed and tested., (© 2024 The Authors. Australasian Journal of Dermatology published by John Wiley & Sons Australia, Ltd on behalf of Australasian College of Dermatologists.)

Purpose: This paper aims to explore how an academic researcher and a practitioner experience scepticism for their qualitative research. Design/methodology/approach: The study applies Olt and Teman's new conceptual phenomenological polyethnography (2019) methodology, a hybrid of phenomenology and duoethnography. Findings: For the researcher-participants, the essence of living with scepticism means feeling a sense of injustice; struggling with the desire for simplicity and quantification; being in a circle of uneasiness; having a survival mechanism; and embracing healthy scepticism. They experience the essence differently and similarly in varied cultural contexts. Through duoethnographic conversations, they acknowledge that while there can be scepticism of their work, it is important to remain sceptical, persistent and curious by challenging traditional concepts. Theoretical and practical advances in artificial intelligence (AI) continue to highlight the need for clarifying qualitative researcher roles in academia and practice. Originality/value: This paper contributes to the debate of qualitative versus quantitative research. Its originality is in exploring scepticism as lived experience, from an academic and practitioner perspective and applying a phenomenological polyethnography approach that blends two different traditional research paradigms.

Purpose: The purpose of this paper is to share findings from empirically driven conceptual research into the implications for English teachers of understanding generative AI as a "tool" for writing. Design/methodology/approach: The paper reports early findings from an Australian National Survey of English teachers and interrogates the notion of the AI writer as "tool" through intersectional feminist discursive-material analysis of the metaphorical entailments of the term. Findings: Through this work, the authors have developed the concept of "coloniser tool-thinking" and juxtaposed it with First Nations and feminist understandings of "tools" and "objects" to demonstrate risks to the pursuit of social and planetary justice through understanding generative AI as a tool for English teachers and students. Originality/value: Bringing together white and First Nations English researchers in dialogue, the paper contributes a unique perspective to challenge widespread and common-sense use of "tool" for generative AI services.

For over two decades now, the application of Citizen Science to Education has been evolving, and fundamental topics, such as the drivers of motivation to participate in Citizen Science projects, are still under discussion. Some recent developments, though, like the use of Artificial Intelligence to support data collection and validation, seem to point to a clear-cut divergence from the mainstream research path. The objective of this paper is to summarise the development trajectory of research on Citizen Science in Education so far, and then shed light on its future development, to help researchers direct their efforts towards the most promising open questions in this field. We achieved these objectives by using the lens of the Affordance-Actualisation theory and the Main Path Analysis method.

Purpose: Foresight J's journey started in 1999, and in 2022, it marked the conclusion of its 24 years of publication. This paper aims to provide an overall overview of important research trends published in Foresight J between 1999 and 2022 by conducting a quantitative analysis of the journal's literature. The overarching goal is to provide valuable insights into the dynamics of scholarly communication, aiding researchers, institutions and policymakers in assessing the significance and influence of academic work, guiding future research directions and academic evaluation. Design/methodology/approach: The two bibliometrics methodologies that make up the methodology of this article are scientific mapping and performance analysis. Authors have explained the development and composition of the Foresight J using these methods. The SCOPUS database is being used in current research to analyse several dimensions, such as the evolution of publications by year, the most cited papers, core authors and researchers, leading countries and prolific institutions. Moreover, the conceptual structure, scope, burst detection and co-occurrence analysis of the journal are mapped using network visualization software such as VOSviewer, CiteSpace and RStudio. Findings: With a strong track record of output over the years, Foresight J has continued to develop in terms of publications. It is determined that "Saritas" is the author with the greatest overall impact. However, according to SCOPUS bibliometric data, "Blackman" and "Richardson" are the authors with the greatest relevance in terms of the quantity of articles. In addition, it becomes apparent that the USA, Australia and the UK are very productive nations in terms of publications. The most popular fields of the journal have always been forecasting, foresight, scenario planning, strategic planning, decision-making, technology and sustainable development. These are also the author keywords that appear the most frequently. In contrast, new study themes in the Foresight J include digital technologies, innovation, sustainability, blockchain, artificial intelligence and sustainability. Research limitations/implications: Several noteworthy research implications are provided by the bibliometric study of Foresight J. "Saritas" is the author with the most overall impact, indicating that the precise contributions and influence of this researcher in the fields of forecasting, foresight and related fields. Given that "Blackman" and "Richardson" are well-known writers, it is also critical to examine the scope and complexity of their contributions to potentially identify recurring themes or patterns in their writing. The geographic productivity results, which show that the USA, Australia and the UK are the top three countries for Foresight J publications, may encourage more research into regional differences, patterns of collaboration and the worldwide distribution of research endeavours in the context of forecasting and foresight. Popular fields including scenario planning, forecasting, foresight and sustainable development are consistent, indicating persistent research interests. Examining the causes of these subjects' ongoing relevance can reveal information about the consistency and development of scholarly interests over time. Practical implications: Foresight J's bibliometric analysis has real-world applications for many stakeholders. It helps editors and publishers make strategic decisions about outreach and content by providing insights regarding the journal's influence. Assessing organizational and author productivity helps institutions allocate resources more effectively. Policymakers acquire an instrument to evaluate research patterns and distribute funds efficiently. In general, bibliometric study of a journal helps decisionmakers in academic publishing make well-informed choices that maximize the potential of options for authors, editors, institutions and policymakers. Social implications: The societal ramifications of bibliometrically analysing Foresight J from 1999 and 2022 are substantial. This analysis highlights, over the past 24 years, research trends, technological developments and societal priorities have changed by methodically looking through the journal's articles. Gaining knowledge about the academic environment covered by the journal can help raise public awareness of important topics and promote critical thinking. In addition, the analysis can support evidence-based decision-making by alerting decision makers to the influential research that was published in Foresight J. This could have an impact on the course of policies pertaining to innovation, technology and societal development. Originality/value: This study presents a first comprehensive article that provides a general overview of the main trends and patterns of the research over the Foresight J's history since its inception. Also, the paper will help the scientific community to know the value and impact of Foresight J. [ABSTRACT FROM AUTHOR]

The transformation of cities into smarter and more efficient environments relies on proactive and timely detection and maintenance of city-wide infrastructure, including roadside infrastructure such as road signs and the cleaning of illegally dumped rubbish. Currently, these maintenance tasks rely predominantly on citizen reports or on-site checks by council staff. However, this approach has been shown to be time-consuming and highly costly, resulting in significant delays that negatively impact communities. This paper presents AIoT-CitySense, an AI and IoT-driven city-scale sensing framework, developed and piloted in collaboration with a local government in Australia. AIoT-CitySense has been designed to address the unique requirements of roadside infrastructure maintenance within the local government municipality. A tailored solution of AIoT-CitySense has been deployed on existing waste service trucks that cover a road network of approximately 100 kms in the municipality. Our analysis shows that proactive detection for roadside infrastructure maintenance using our solution reached an impressive 85%, surpassing the timeframes associated with manual reporting processes. AIoT-CitySense can potentially transform various domains, such as efficient detection of potholes and precise line marking for pedestrians. This paper exemplifies the power of leveraging city-wide data using AI and IoT technologies to drive tangible changes and improve the quality of city life. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever‑growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi‑society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: APB: member of the Insights into Imaging Scientific Editorial Board. He has not taken part in the review or selection process of this article. BA: No competing interests. JC: No competing interests. EK: Shareholder Gleamer, Paris and Contextflow, Vienna. NK: Consultant for ES3 (aerospace company), consultant for Synapsica Healthcare, partner (equity owner) at Radiology Partners (RP), sole or partial owner of several radiology practices managed by RP. RP has a minority interest in AIDOC. RP has an indirect minority interest in Rad AI. Associate Fellow Stanford AIMI Center. Hold several volunteer positions at RSNA, ACR, SIIM and RADequal. JM: Consultant, Microsoft (Nuance), Research funding, royalties, GE, Research funding, Siemens, Spouse employment, shareholder Annexon Biosciences, Spouse employment Bristol Meyers Squibb. LOR: No competing interests. DPDS: member of the Insights into Imaging Scientific Editorial Board. He has not taken part in the review or selection process of this article. AT: No competing interests. CW: Chair, Commission on Informatics and Member, Board of Chancellors, American College of Radiology. Advisor: Notable Systems, and RadPair. JS: No competing interests.

In recent years, mine site closure and rehabilitation have emerged as significant global challenges. The escalating number of abandoned mines, exemplified by over 60,000 in Australia in 2017, underscores the urgency. Growing public concerns and governmental focus on environmental issues are now jeopardising sustainable mining practices. This paper assesses the role of unmanned aerial vehicles (UAVs) in mine closure, exploring sensor technology, artificial intelligence (AI), and mixed reality (MR) applications. Prior research validates UAV efficacy in mining, introducing various deployable sensors. Some studies delve into AI's use for UAV data analysis, but a comprehensive review integrating AI algorithms with MR methods for mine rehabilitation is lacking. The paper discusses data acquisition methods, repeatability, and barriers toward fully autonomous monitoring systems for mine closure projects. While UAVs prove adaptable with various sensors, constraints such as battery life and payload capacity impact effectiveness. Although UAVs hold potential for AI testing in mine closure studies, these applications have been overlooked. AI algorithms are pivotal for creating autonomous systems, reducing operator intervention. Moreover, MR's significance in mine closure is evident, emphasising its application in the mining industry. Ultimately, a hybrid UAV–AI–MR technology is not only viable but essential for achieving successful mine closure and sustainable mining practices in the future. [ABSTRACT FROM AUTHOR]

The aim of this paper is to explore what aspects create experiential value for wine tourists. We synthesize the extant literature into four dimensions for wine tourism value creation, namely, product-related aspects; sensory and affective experiential aspects; cognitive, educational experiential aspects; and social-relational experiential value-creating aspects. So far, most studies merely discuss product-related aspects whilst insights on experiential value are less known. Using online review data from wine tourists in Australia, we develop a novel deep neural network-based framework using an innovative AI-based exploratory design. Results of the case study reveal that in addition to product-related aspects, sensory-and education-related experiential aspects are also highly important for value creation in wine tourism. Theoretical and practical implications, as well as ideas for future research are discussed. [ABSTRACT FROM AUTHOR]

Objectives: This study explored the familiarity, perceptions and confidence of Australian radiology clinicians involved in reading screening mammograms, regarding artificial intelligence (AI) applications in breast cancer detection. Methods: Sixty-five radiologists, breast physicians and radiology trainees participated in an online survey that consisted of 23 multiple choice questions asking about their experience and familiarity with AI products. Furthermore, the survey asked about their confidence in using AI outputs and their preference for AI modes applied in a breast screening context. Participants' responses to questions were compared using Pearson's χ 2 test. Bonferroni-adjusted significance tests were used for pairwise comparisons. Results: Fifty-five percent of respondents had experience with AI in their workplaces, with automatic density measurement powered by machine learning being the most familiar AI product (69.4%). The top AI outputs with the highest ranks of perceived confidence were 'Displaying suspicious areas on mammograms with the percentage of cancer possibility' (67.8%) and 'Automatic mammogram classification (normal, benign, cancer, uncertain)' (64.6%). Radiology and breast physicians preferred using AI as second-reader mode (75.4% saying 'somewhat happy' to 'extremely happy') over triage (47.7%), pre-screening and first-reader modes (both with 26.2%) (P < 0.001). Conclusion: The majority of screen readers expressed increased confidence in utilising AI for highlighting suspicious areas on mammograms and for automatically classifying mammograms. They considered AI as an optimal second-reader mode being the most ideal use in a screening program. The findings provide valuable insights into the familiarities and expectations of radiologists and breast clinicians for the AI products that can enhance the effectiveness of the breast cancer screening programs, benefitting both healthcare professionals and patients alike. What is known about the topic? Artificial intelligence (AI) holds promise in providing computer-aided detection in health care, however, current research suggests that standalone AI applications in clinical practice fall short of matching the accuracy of a single radiologist. What does this paper add? The study showed a significant preference among clinicians for using AI as a supplementary tool, serving as a second-reader. Such an integrated approach, where AI aids in flagging suspicious areas on mammograms or offers automatic classification, reflects the ideal cooperation between breast screening readers and AI systems. What are the implications for practitioners? These insights shed light on clinicians' familiarity with and expectations of AI tools that can boost the effectiveness of breast screening programs. [ABSTRACT FROM AUTHOR]

Simple Summary: Breast cancer is one of the leading causes of cancer-related death in women. The early detection of breast cancer with screening mammograms plays a pivotal role in reducing mortality rates. Although population-based double-reading screening mammograms have reduced mortality by over 31% in women with breast cancer in Europe, continuing this program is difficult due to the shortage of radiologists. Artificial intelligence (AI) is an emerging technology that has provided promising results in medical imaging for disease detection. This study investigates the performance of AI models on an Australian mammographic database, demonstrating how transfer learning from a USA mammographic database to an Australian one, contrast enhancement on mammographic images and the quality of training data according to radiologists' concordance can improve breast cancer diagnosis. Our proposed methodology offers a more efficacious approach for AI to contribute to radiologists' decision making when interpreting mammography images. This paper investigates the adaptability of four state-of-the-art artificial intelligence (AI) models to the Australian mammographic context through transfer learning, explores the impact of image enhancement on model performance and analyses the relationship between AI outputs and histopathological features for clinical relevance and accuracy assessment. A total of 1712 screening mammograms (n = 856 cancer cases and n = 856 matched normal cases) were used in this study. The 856 cases with cancer lesions were annotated by two expert radiologists and the level of concordance between their annotations was used to establish two sets: a 'high-concordances subset' with 99% agreement of cancer location and an 'entire dataset' with all cases included. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of Globally aware Multiple Instance Classifier (GMIC), Global-Local Activation Maps (GLAM), I&H and End2End AI models, both in the pretrained and transfer learning modes, with and without applying the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm. The four AI models with and without transfer learning in the high-concordance subset outperformed those in the entire dataset. Applying the CLAHE algorithm to mammograms improved the performance of the AI models. In the high-concordance subset with the transfer learning and CLAHE algorithm applied, the AUC of the GMIC model was highest (0.912), followed by the GLAM model (0.909), I&H (0.893) and End2End (0.875). There were significant differences (p < 0.05) in the performances of the four AI models between the high-concordance subset and the entire dataset. The AI models demonstrated significant differences in malignancy probability concerning different tumour size categories in mammograms. The performance of AI models was affected by several factors such as concordance classification, image enhancement and transfer learning. Mammograms with a strong concordance with radiologists' annotations, applying image enhancement and transfer learning could enhance the accuracy of AI models. [ABSTRACT FROM AUTHOR]

Subsurface channels are stratigraphic features in seismic data that can act as reservoirs or conduits for hydrocarbons. However, detecting and characterizing these channels is challenging due to the limitations of seismic resolution and the complexity of the subsurface geology. Seismic inversion is a technique that can enhance the seismic data by transforming the seismic traces into quantitative estimates such as acoustic impedance (AI), which is a key reservoir rock property. AI inversion can help to identify and delineate the subsurface channels by providing more contrast and detail of the channel geometry, fill, and surrounding sediments. Seismic inversion is often challenged by the non-uniqueness, ambiguity and uncertainty of the inversion results due to noise and band-limited data. This paper uses a fuzzy model-based seismic inversion method that integrates prior information and fuzzy clustering constraints to produce more realistic and reliable AI models. This method assigns data points to multiple clusters with varying degrees of membership, which can capture the overlapping of AI values of different geological formations. The method is applied to the 3D Poseidon seismic data from the Browse Basin, offshore Western Australia, and the results are compared with those of conventional model-based inversion. Since there is no well-data in an interest channel zone, a qualitative evaluation with seismic attributes is performed. The subsurface structures are further interpreted by various seismic attributes. The comparison shows that the fuzzy model-based inversion method can improve the resolution, contrast and stability of the AI models and reveal more detail of the subsurface geology. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) tools, notably ChatGPT, are increasingly recognised for their transformative potential in higher education. This study employs a detailed case study approach complemented by a survey, delving into ChatGPT's impact on pedagogical practices, student engagement, and academic performance. It involved 74 undergraduate and postgraduate students enrolled in data analytics courses in Australia. The quantitative analysis highlights ChatGPT's role in providing personalised and on-demand support, which is highly valued among users for its flexibility and responsiveness, meeting a critical demand in educational settings. Notably, the study identifies a medium effect size ( η 2 = 0.173 ) in perceived benefits, indicating that ChatGPT accounts for approximately 17.3% of the variance in improved academic outcomes. However, challenges such as ChatGPT's limited understanding of complex queries and the lack of human interactions are primary concerns, with a medium effect size ( η 2 = 0.289 ) suggesting significant areas for improvement. Furthermore, statistical analyses reveal a clear relationship between the frequency of ChatGPT usage and the perception of its benefits, underscoring the transformative potential for users who have integrated it into their academic practices. Despite these challenges, the differential impact on users versus non-users highlights the potential for ChatGPT to foster more engaging and effective educational practices. The findings advocate for targeted strategies to epitomise ChatGPT's integration into educational settings, emphasising the need for ongoing research and the development of comprehensive guidelines to navigate its complexities and maximise its educational benefits. [ABSTRACT FROM AUTHOR]

Automation and artificial intelligence (AI) is already possible for many radiation therapy planning and treatment processes with the aim of improving workflows and increasing efficiency in radiation oncology departments. Currently, AI technology is advancing at an exponential rate, as are its applications in radiation oncology. This commentary highlights the way AI has begun to impact radiation therapy treatment planning and looks ahead to potential future developments in this space. Historically, radiation therapist's (RT's) role has evolved alongside the adoption of new technology. In Australia, RTs have key clinical roles in both planning and treatment delivery and have been integral in the implementation of automated solutions for both areas. They will need to continue to be informed, to adapt and to transform with AI technologies implemented into clinical practice in radiation oncology departments. RTs will play an important role in how AI‐based automation is implemented into practice in Australia, ensuring its application can truly enable personalised and higher‐quality treatment for patients. To inform and optimise utilisation of AI, research should not only focus on clinical outcomes but also AI's impact on professional roles, responsibilities and service delivery. Increased efficiencies in the radiation therapy workflow and workforce need to maintain safe improvements in practice and should not come at the cost of creativity, innovation, oversight and safety. [ABSTRACT FROM AUTHOR]

Recent failures of bridges worldwide highlight the need for robust structural health surveillance systems to provide alerts for early damage and prevent severe loss. Structural health monitoring (SHM) continues to evolve and gain wide adoption among asset managers to mitigate catastrophic failure risks of structural systems. With the prolific integration of SHM with artificial intelligence and internet of things systems becoming prevalent, several data-driven approaches have gained popularity in SHM. However, it is challenging to obtain ground-truth-labelled damage samples for supervised machine learning methods. A class of shallow machine learning methods known as unsupervised methods (e.g. the one-class support vector machine (OCSVM)), which only uses unlabelled data from a healthy state, has been shown to achieve promising results. However, shallow machine learning methods require laborious expert-crafted feature inputs for optimal performance. Inspired by the automatic feature-extraction ability of deep neural networks, unsupervised methods (deep learning models trained without supervisory ground truth labels) such as autoencoder (AE)-based methods were explored to detect structural damage. Results obtained on both real-world data from Sydney Harbour Bridge, one of Australia's iconic structures, and data collected from laboratory specimens demonstrate the effectiveness of the AE-based method over conventional shallow methods such as the OCSVM. [ABSTRACT FROM AUTHOR]

Background: Chat Generative Pre‐trained Transformer (ChatGPT) is a freely available online artificial intelligence (AI) program capable of understanding and generating human‐like language. This study assessed veterinary students' perceptions about ChatGPT in education and practice. It compared perceptions about ChatGPT between students who had completed a critical analysis task and those who had not. Methods: This cross‐sectional study surveyed 498 Doctor of Veterinary Medicine (DVM) students at The University of Sydney, Australia. Second‐year DVM students researched a veterinary pathogen and then completed a critical analysis of ChatGPT (version 3.5) output for the same pathogen. A survey based on the Technology Acceptance Model was then delivered to all DVM students from all years of the programme, collecting data using Likert‐style, categorical and free‐text items. Results: Over 75% of the 100 respondents reported having used ChatGPT. The students found ChatGPT's output relevant and practical for their use but perceived it as inaccurate. They perceived ChatGPT output to be more useful for veterinary students than for pet owners or veterinarians. Those who had completed the critical analysis assignment had a more positive view of ChatGPT's practicality for veterinary students but noted its authoritative tone even when delivering inaccurate information. Over 50% of the students agreed that information about tools such as ChatGPT should be included in the veterinary curriculum. Students agreed that veterinarians should embrace AI but disagreed that AI would eventually replace the need for veterinarians. Conclusions: A critical appraisal of outputs from AI tools such as ChatGPT may help prepare future veterinarians for the effective use of these tools. [ABSTRACT FROM AUTHOR]

The launch of OpenAI's ChatGPT model in late 2022, as most Australian universities wound down for summer holidays, elicited varied responses from higher education practitioners, policy makers and commentators that ranged from heightened concern and proscriptive impulses through to cautious excitement about the potentially disruptive, deceptive impact of university student use of AI chatbots (Skeat and Ziebell, 2023). Generative AI has both transformative and disruptive implications for conventional university assessment practices. Simultaneously, we observed a tension between university teaching and learning imperatives of digital literacy, academic integrity, student employability, and data security and privacy. Large Language Models (LLMs) run on deep learning programming, trained to process data in a way modelled on human brain cognition, to generate human-like responses to natural language prompts. Generative AI can answer and compose questions, write narratives, summarise documents, and construct essays, reports, reviews etc, and perform reflective writing capabilities (Li et al., 2023). Importantly, generative AI performs these tasks with substantially different degrees of accuracy, biases, and relevance potentially with each prompt. These dynamic and iterative learning abilities have significantly, sometimes imperceptibly, compromised the integrity and reliability of conventional university assessment types. Moreover, generative AI is improving incrementally, increasingly integrated into everyday software, platforms and apps (Liu and Bridgeman, June 2023). Nor is it only traditional written assessments that are at risk of disruption and invalidation. AI image generators like OpenAI's DALL-E can produce high-quality, realistic and fantastical artworks. ChatGPT-like AI models are designed for conversational and dialogic user experiences, programmed on natural and intuitive patterns of language use. Even without any targeted training in ethical, effective and critical 'prompt engineering' (cf. Liu, 2023) students can output passable assessment content. As well as concerns around digital literacy, academic integrity and meaningful learning, prompting, performed rudimentarily at least, blurs the lines between a student's original thinking (and integration of sources) and machine-generated output. The foundational challenge being in determining whether a student's submission is a result of their applied understanding or the AI's algorithmic capabilities. Yet, this GenAI interactional, iterative user experience can also be harnessed by educators to design, facilitate and assess socially constructivist, authentic, analytical, and innovative approaches to student learning (Liu and Bridgeman, June 2023). We report on a research project that implemented an iterative, nested, and collaborative assessment redesign (Lodge et al. 2023) as an alternative to a 2000-word Final Research Report due in the semester's penultimate week. For the redesign, we partially broke the one submission down into three, smaller critical reflections due across a semester. For the first, students used ChatGPT before, and then after, learning a prompt engineering approach (cf. Liu, 2023). Secondly, students reflected on their engagement with generative AI as a collaborator in comparison to their collaboration with peers on a task. The final critical reflection required students to anticipate how generative AI might impact their professional practices drawing on the subject's key topics. With ethics approval granted, our research findings are drawn from the roughly 10% of all students (n = 83) that chose the redesigned option. We analyse their three submissions in terms of existing themes in the literature (cf. Skeat and Ziebell, 2023) around academic integrity, digital literacy, institutional messaging and student belonging, and generative AI as 'study buddy' (Skeat and Ziebell, 2023). [ABSTRACT FROM AUTHOR]