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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.

Chatbots have experienced significant growth over the past decade, with a proliferation of new applications across various domains. Previous studies also demonstrate the trend of new technologies, especially artificial intelligence, being adopted in libraries. The purpose of this study is to determine the current research priorities and findings in the field of chatbots in libraries. A systematic literature review was performed utilising the PRISMA checklist and the databases Scopus and Web of Science, identifying 5734 records. Upon conducting the first screening, abstract screening, full-text assessment, and quality assessments guided by the CASP appraisal checklist, 19 papers were deemed suitable for inclusion in the review. The results of the review indicate that the majority of the existing studies were empirical in nature (primarily adopting qualitative methods) and technology reviews with a focus on reviewing the implementation and maintenance, design, evaluation, characteristics, and application of chatbots. The chatbots of interest were mainly text-based and guided chatbots, with closed-source tools with access portals mostly built on library web pages or integrated with social software. The research findings primarily concerned the development models and necessary tools and technologies, the application of chatbots in libraries. Our systematic review also suggests that studies on chatbots in libraries are still in the early stages. [This paper was presented at the 2023 Libraries in the Digital Age (LIDA) International Conference (Osijek, Croatia, May 24-26, 2023).]

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.)

Recent negotiations of 'data' in schools place focus on student assessment and NAPLAN. However, with the rise in artificial intelligence (AI) underpinning educational technology, there is a need to shift focus towards the value of teachers' digital data. By doing so, the broader debate surrounding the implications of these technologies and rights within the classroom as a workplace becomes more apparent to practitioners and educational researchers. Drawing on the Australian Human Rights Commission's "Human Rights and Technology final report," this conceptual paper focusses on teachers' rights alongside emerging technologies that use or provide predictive analytics or artificial intelligence, also called 'personalisation'. The lens of Postdigital positionality guides the discussion. Three potential consequences are presented as provocations: (1) What might happen if emerging technology uses teachers' digital data that represent current societal inequality? (2) What might happen if insights provided by such technology are inaccurate, insufficient, or unrepresentative of our teachers? (3) What might happen if the design of the AI system itself is discriminatory? This conceptual paper argues for increased discourse about technologies that use or provide predictive analytics complemented by considering potential consequences associated with algorithmic bias.

The launch of new tools in late 2022 heralded significant growth in attention to the impacts of generative AI (GenAI) in education. Claims of the potential impact on education are contested, but there are clear risks of inappropriate use particularly where GenAI aligns poorly with learning aims. In response, in mid-2023, the Australian Federal Government held an inquiry, calling for public submissions. This inquiry offers a lens onto the policy framing of GenAI in education and provides the object of investigation for this paper. We use the inquiry submissions, extracting structured claims from each. This extraction is provided as an open data set for further research, while this paper focuses on our analysis of the policy recommendations made.

Emergence and development of the blockchain technology, which is able to transform into "a most powerful disruptive innovation", shall definitely concern universities. Moreover, nowadays the blockchain technology meets the challenges that both the system of higher education and the entire society are currently facing. Advantages of the blockchain technology are decentralized open data, absence of forgeries, safe storage of information, and reduction of transaction expenses related to data checkup, control, and verification. This paper provides a critical analysis of application of the blockchain technology considering with its applicability opportunities and restrictions in education; it also aims to identify the consequences of its influence upon the development of education. The article analyzes real cases when this technology was applied, with the Massachusetts Institute of Technology (MIT) as an example. The MIT applied it to protect and validate the certificates that it issued. Another example is the Sony Global Education that forms individual data on its trainees' competencies and productivity; a third one relates to the University of Nicosia, which was the first to use smart contracts and accept cryptocurrency as a form of payment. The paper also considers the elements of the blockchain technology at universities (both in Russia and outside it), which participate in massive open online courses. It determines the scope of application of this technology in the Russian educational system. In addition, this article provides a literature review related to application of the blockchain technology; the review includes works by such renowned researchers as D. Tapscott, B. Bleir, A. Watters, A. Grech, A. Camilleri, M. Swan, A. Zaslavsky, etc. The paper analyzes the obtained findings of the survey that its authors have conducted among experts, professors, and specialists involved in accreditation. Thus, the paper provides an analysis of opportunities and restrictions related to application of the blockchain technology in higher education.

While ChatGPT has recently become very popular, AI has a long history and philosophy. This paper intends to explore the promises and pitfalls of the Generative Pre-trained Transformer (GPT) AI and potentially future technologies by adopting a speculative methodology. Speculative future narratives with a specific focus on educational contexts are provided in an attempt to identify emerging themes and discuss their implications for education in the 21st century. Affordances of (using) AI in Education (AIEd) and possible adverse effects are identified and discussed which emerge from the narratives. It is argued that now is the best of times to define human vs AI contribution to education because AI can accomplish more and more educational activities that used to be the prerogative of human educators. Therefore, it is imperative to rethink the respective roles of technology and human educators in education with a future-oriented mindset.

This paper explores the pedagogical potential of Generative Artificial Intelligence (GenAI) in secondary education through a dialogic approach to teaching, learning and assessment. It presents an ongoing action research project in collaboration with a high school in Western Australia, involving four teachers to integrate GenAI in their classrooms. The study aims to develop and evaluate innovative pedagogies for leveraging GenAI to enhance educational practices and student learning outcomes across three action research teams focusing on critical questioning, assessment and differentiation. Drawing on Bakhtin's concept of heteroglossia, the study conceptualizes GenAI not as a definitive knowledge provider but as a dialogic agent that facilitates collaborative dialogue and co-construction of knowledge among students. This perspective aims to encourage students to critically engage with AI-generated content and integrate multiple viewpoints into their learning, thus fostering key epistemic skills. Initial findings demonstrate active student engagement in dialogues with GenAI, highlighting the use of follow-up questions that indicate critical thinking and creativity. These findings underscore the significance of integrating multiple perspectives and fostering epistemic skills among students, promoting a comprehensive and ethical approach to AI use in education. The research calls for further exploration of GenAI's pedagogic potential and its broader implications for educational practices, suggesting a promising avenue for pedagogical innovation and the development of critical thinking skills in the digital age.

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.

Learning analytics dashboards commonly visualize data about students with the aim of helping students and educators understand and make informed decisions about the learning process. To assist with making sense of complex and multidimensional data, many learning analytics systems and dashboards have relied strongly on AI algorithms based on predictive analytics. While predictive models have been successful in many domains, there is an increasing realization of the inadequacies of using predictive models in decision-making tasks that affect individuals without human oversight. In this paper, we employ a suite of state-of-the-art algorithms, from the online analytics processing, data mining, and process mining domains, to present an alternative human-in-the-loop AI method to enable educators to identify, explore, and use appropriate interventions for subpopulations of students with the highest deviation in performance or learning process compared to the rest of the class. We demonstrate an application of our proposed approach in an existing learning analytics dashboard (LAD) and explore the recommended drill-downs in a course with 875 students. The demonstration provides an example of the recommendations from real course data and shows how recommendations can lead the user to interesting insights. Furthermore, we demonstrate how our approach can be employed to develop intelligent LADs.

Purpose: This paper aims to demonstrate how skills taxonomies can be used in combination with machine learning to integrate diverse online datasets and reveal skills gaps. The purpose of this study is then to show how the skills gaps revealed by the integrated datasets can be used to achieve better labour market alignment, keep educational offerings up to date and assist graduates to communicate the value of their qualifications. Design/methodology/approach: Using the ESCO taxonomy and natural language processing, this study captures skills data from three types of online data (job ads, course descriptions and resumes), allowing us to compare demand for skills and supply of skills for three different occupations. Findings: This study illustrates three practical applications for the integrated data, showing how they can be used to help workers who are disrupted by technology to identify alternative career pathways, assist educators to identify gaps in their course offerings and support students to communicate the value of their training to employers. Originality/value: This study builds upon existing applications of machine learning (detecting skills from a single dataset) by using the skills taxonomy to integrate three datasets. This study shows how these complementary, big datasets can be integrated to support greater alignment between the needs and offerings of educators, employers and job seekers.

ChatGPT, a sophisticated online chatbot, sent shockwaves through many sectors once reports filtered through that it could pass exams. In higher education, it has raised many questions about the authenticity of assessment and challenges in detecting plagiarism. Amongst the resulting frenetic hubbub, hints of potential opportunities in how ChatGPT could support learning and the development of critical thinking have also emerged. In this paper, we examine how ChatGPT may affect assessment in engineering education by exploring ChatGPT responses to existing assessment prompts from ten subjects across seven Australian universities. We explore the strengths and weaknesses of current assessment practice and discuss opportunities on how ChatGPT can be used to facilitate learning. As artificial intelligence is rapidly improving, this analysis sets a benchmark for ChatGPT's performance as of early 2023 in responding to engineering education assessment prompts. ChatGPT did pass some subjects and excelled with some assessment types. Findings suggest that changes in current practice are needed, as typically with little modification to the input prompts, ChatGPT could generate passable responses to many of the assessments, and it is only going to get better as future versions are trained on larger data sets.

Research on Artificial Intelligence, especially in the field of machine learning, has exploded in the twenty-first century. AI research in universities has long been funded by a combination of government and corporate sources. The funding of AI research in the contemporary university includes technology companies as both funders and generators of research areas. This paper looks at the links between technology companies and AI research in three areas: first, the ways in which technology companies influence both the content and practices of AI research in universities; second, how university research policies enable conditions that blur traditional boundaries between corporate and academic AI research; and third, how an ethos of 'open science', that is increasingly corporatised, moves ideas about AI from universities to companies. We conclude that technology companies influence AI research within established feedback loops in the transformed relationships between economy, society, research, and the contemporary university.

Artificial intelligence (AI) technology is becoming increasingly accurate and prevalent for the diagnosis of skin cancers. Commercially available AI diagnostic software is entering markets across the world posing new legal and ethical challenges for both clinicians and software companies. Australia has the highest rates of skin cancer in the world and is poised to be a significant benefactor and pioneer of the technology. This review describes the legal and ethical considerations raised by the emergence of artificial intelligence in skin cancer diagnosis and proposes recommendations for best practice., (© 2021 The Australasian College of Dermatologists.)

In the last decade education has experienced a shift from privatization to commercialization. This paper argues that the commercialization of education has evolved more recently as a result of artificially intelligent corporate players, enabling forms of insights sales called 'Dark Advertising'. It unpacks how Dark Advertising are profiting from data-driven predictions that reveal where demand is emerging, rather than responding to perceived problems by examining reports by the Australian Competition and Consumer Commission (ACCC). Able to produce techno-solutions 'just in time' through Dark Advertising, Dark Advertising are considered to be enabling new forms of governance and influencing educational policy. Findings of the examination reveal associations in terms of teachers' privacy, ability to provide consent, and agency. Arguably, circumnavigating Codes of Conduct and Privacy legislation, the author calls for greater scrutiny into various information asymmetries associated with Insight Sales strategies that predict, nudge and experiment with teachers' behavior for profit.

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]

Authentic writing is an important aspect in education and research. Unfortunately, academic misconduct occurs among students and researchers. Consequently, written articles undergo certain detection measures and most teaching and research institutions use a range of software to detect plagiarism. However, state-of-the-art Automatic Article Generator (AAG) writing powered by Artificial Intelligence provides a new platform for new types of serious academic misconduct that cannot be easily detected and even if they are detected, can be hard to prove. The main objective of this study is to raise awareness of these tools among academics. This paper first explains the features of AAG writing, then investigates whether academics can distinguish AAG writing from human writing and whether raising the awareness of AAG between academics can improve their ability to detect AAG writing. A case study showed how difficult it is for academics with no knowledge of AAGs to identify this writing. A survey was used to indicate how a training session can improve the ability of detecting AAG writing. The results show that raising awareness training increased the academics' ability to detect AAG writing. Lastly, the possible solutions to mitigate the academic integrity issues associated with AAG writing have been discussed.

Artificial Intelligence has the potential to be an important part of education governance. It is already being built into everything from business intelligence platforms to real-time online testing. In this paper, we aim to understand how AI becomes, and forms, a legitimate part of authority in contemporary education governance in what we call the automated education governance assemblage, that incorporates technology companies and AI-supported products used in education. We focus on EduTech Australia -- an education technology trade show in Sydney -- as a way to look at: (1) how the different aspects of automated governance are connected at EduTech, including the relations between different participants, companies and products; and (2) how the automated governance assemblage works to legitimise and constitute EduTech as a policy space and site of new authorities in education governance.

Purpose: This paper aims to present a comprehensive review of the present state and trends of smart education research. It addresses the need to have a systematic review of smart education to depict its research landscape in view of the growing volume of related publications. Design/methodology/approach: A bibliometric analysis of publications on smart education published in 2011 to 2020 was conducted, covering their patterns and trends in terms of collaboration, key publications, major topics and trends. A total of 1,317 publications with 29,317 cited references were collected from the Web of Science and Scopus for the bibliometric analysis. Findings: Research on smart education has been widely published in various sources. The most frequently cited references are all theoretical or discussion articles. Researchers in the USA, China, South Korea, India and Russia have been most active in research collaborations. However, international collaborations have remained infrequent except for those involving the USA. The research on smart education broadly covered smart technologies as well as teaching and learning. The emerging topics have addressed areas such as the Internet of Things, big data, flipped learning and gamification. Originality/value: This study depicts the intellectual landscape of smart education research, and illustrated the evolution and emerging trends in the field. The results highlight its latest developments and research needs, and suggest future work related to research collaborations on a larger scale and more studies on smart pedagogies.

This editorial introduces the September issue and highlights four papers that are concerned with automatically detecting fake news and “phishing” attempts. The discussion is in the context of a recent proposal by the Australian Government to restrict the spread of misinformation and disinformation. It is noted that all the proposed methods use Artificial Intelligence (AI), suggesting that bias, which can be introduced by AI training processes, would be worthy of further research. The other papers are briefly described. This issue also includes an obituary for Harry Wragge, a former head of Telecom/Telstra Research Laboratories. We also note the passing of John Burke, an influential member of this Journal’s Editorial Advisory Board. [ABSTRACT FROM AUTHOR]

New cognitive infrastructures are emerging as digital platforms and artificial intelligence enable new forms of automated thinking that shape human decision-making. This paper (a) offers a new theoretical perspective on automated thinking in education policy and (b) illustrates how automated thinking is emerging in one specific policy context. We report on a case study of a policy analysis unit ('The Centre') in an Australian state education department that has been implementing a BI strategy since 2013. The Centre is now focused on using BI to support complex decision making and improve learning outcomes, and their strategy describes this focus as becoming 'information centric'. The theoretical framework for our analysis draws on infrastructure studies and philosophy of technology, particularly Luciana Parisi's recent work on automated thinking. We analyse technical documentation and semi-structured interview data to describe the enactment of a BI strategy in The Centre, with a focus on how new approaches to data analytics are shaping decision-making. Our analysis shows that The Centre is developing a cognitive infrastructure that is already creating new conditions for education policy making, and we conclude with a call for research designs that enable pragmatic exploration of what these infrastructures can do.

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]

The following offers an overview of what it means for cities to be "smart." It draws the supporting definitions and critical insights into smart cities from a series of papers presented at the 2009 Trans-national Conference on Creating Smart(er) Cities. What the papers all have in common is their desire to overcome the all too often self-congratulatory nature of the claims cities make to be smart and their over-reliance on a distinctively entrepreneurial route to smart cities. Individually, they serve to highlight the major challenges cities face in their drive to become smart. Collectively they begin to uncover what it means for cities to be smart. Together the papers offer an alternative route to smart cities laid down by those advocating a more neo-liberal roadmap, rooted in a critically aware knowledge-base and more realistic understanding of what it means for cities to be smart(er). (Contains 1 note and 1 figure.)

Mathematics competency is fast becoming an essential requirement in ever greater parts of day-to-day work and life. Thus, creating strategies for improving mathematics learning in students is a major goal of education research. However, doing so requires an ability to look at many aspects of mathematics learning, such as demographics and psychological dispositions, in an integrated way as part of the same system. Large-scale assessments such as the Programme for International Student Assessment (PISA) provide an accessible and large volume of coherent data, and this gives researchers the opportunity to employ data-driven approaches to gain an overview of the system. For these reasons, we have used machine learning to explore the relationships between psychological dispositions and mathematical literacy in Australian 15-year-olds using the PISA 2012 data set. Our results from this strongly data-driven approach re-affirm the primacy of mathematics self-efficacy and highlight novel complex interactions between mathematics self-efficacy, mathematics anxiety and socio-economic status. In this paper, we demonstrate how education researchers can usefully employ data-driven modelling techniques to find complex non-linear relationships and novel interactions in a multidimensional data set.

Aim: This qualitative study aims to explore the perspectives of nursing students regarding the application and integration of generative Artificial Intelligence (AI) tools in their studies., Background: With the increasing prevalence of generative AI tools in academic settings, there is a growing interest in their use among students for learning and assessments., Design: Employing a qualitative descriptive design, this study used semi-structured interviews with nursing students to capture the nuanced insights of the participants., Methods: Semi-structured interviews were digitally recorded and then transcribed verbatim. The research team reviewed all the data independently and then convened to discuss and reach a consensus on the identified themes., Results: This study was conducted within the discipline of nursing at a regional Australian university. Thirteen nursing students, from both first and second year of the programme, were interviewed as part of this study. Six distinct themes emerged from the data analysis, including the educational impact of AI tools, equitable learning environment, ethical considerations of AI use, technology integration, safe and practical utility and generational differences., Conclusions: This initial exploration sheds light on the diverse perspectives of nursing students concerning the incorporation of generative AI tools in their education. It underscores the potential for both positive contributions and challenges associated with the integration of generative AI in nursing education and practice., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Background: Malnutrition is a prevalent issue in aged care facilities (RACFs), leading to adverse health outcomes. The ability to efficiently extract key clinical information from a large volume of data in electronic health records (EHR) can improve understanding about the extent of the problem and developing effective interventions. This research aimed to test the efficacy of zero-shot prompt engineering applied to generative artificial intelligence (AI) models on their own and in combination with retrieval augmented generation (RAG), for the automating tasks of summarizing both structured and unstructured data in EHR and extracting important malnutrition information., Methodology: We utilized Llama 2 13B model with zero-shot prompting. The dataset comprises unstructured and structured EHRs related to malnutrition management in 40 Australian RACFs. We employed zero-shot learning to the model alone first, then combined it with RAG to accomplish two tasks: generate structured summaries about the nutritional status of a client and extract key information about malnutrition risk factors. We utilized 25 notes in the first task and 1,399 in the second task. We evaluated the model's output of each task manually against a gold standard dataset., Result: The evaluation outcomes indicated that zero-shot learning applied to generative AI model is highly effective in summarizing and extracting information about nutritional status of RACFs' clients. The generated summaries provided concise and accurate representation of the original data with an overall accuracy of 93.25%. The addition of RAG improved the summarization process, leading to a 6% increase and achieving an accuracy of 99.25%. The model also proved its capability in extracting risk factors with an accuracy of 90%. However, adding RAG did not further improve accuracy in this task. Overall, the model has shown a robust performance when information was explicitly stated in the notes; however, it could encounter hallucination limitations, particularly when details were not explicitly provided., Conclusion: This study demonstrates the high performance and limitations of applying zero-shot learning to generative AI models to automatic generation of structured summarization of EHRs data and extracting key clinical information. The inclusion of the RAG approach improved the model performance and mitigated the hallucination problem., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

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. KEY POINTS., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

In this paper, we investigate an application of virtual reality and artificial intelligence (AI) as a technological combination that has a potential to improve the learning experience and engage with the modern generation of students. To address this need, we have created a virtual reality replica of one of humanity's first cities, the city of Uruk and populated this city with AI-controlled 3D avatars, which re-enact everyday life of ancient Sumerians in the period around 3000 B.C. Our hypothesis is that by immersing students into this environment and allowing them to learn by browsing through it and interacting with its virtual citizens can be more engaging and motivating than simply reading the corresponding history text or watching an educational video. To confirm this assumption, we have designed a study with three groups of students. One group was given a historical text about Uruk and everyday life of its citizens (created by our subject matter experts), the second group was shown a documentary video on Uruk and the third group was immersed into virtual Uruk and engaged into interactions with its virtual inhabitants. The outcomes of the study suggest that not only did people in the third group provide much more positive qualitative feedback about the learning experience, but they also showed a better comprehension of the study material by performing (on average) 20% better than the first two groups on the mini-exam that was conducted as a part of this study.

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]

Purpose: With the increased demand for the application of technology in property activities, there is a growing need for property professionals adept in using digital technology. Hence, it is important to assess the competence of academia in equipping property professionals with digital technology skills. This study, therefore, assesses property academics in Australian universities to identify their level of knowledge and use of digital technology applicable to the property industry. Design/methodology/approach: Online questionnaire surveys were administered to 22 out of 110 property academics contacted through the Australia Property Institute (API) database to achieve this aim. The collected data were analysed using mean score ranking and ANOVA. Findings: The study found that apart from databases and analytics platforms such as Corelogic RP data, price finder and industry-based software such as the Microsoft Office suite and ARGUS software, the academics were not knowledgeable in most identified and sampled proptech tools. Similarly, most proptech tools were not used or taught to the students. It was also found that early career academics (below five years in academia) were the most knowledgeable group about the proptech tools. Research limitations/implications: Relying on the API database to contact property academics potentially excludes the position of property academics who may not be affiliated or have contacts with API, hence, the findings of this study should be generalised with caution. Practical implications: The study bears huge implications for the property education sector and industry in Australia; a low knowledge and use of nascent tools such as artificial intelligence, machine learning, blockchain, drones, fintech, which have received intense interest, reveals some level of skill gap of students who pass through that system and may need to be upskilled by employers to meet the current day demand. Originality/value: In response to the clamour for technology-inclined property professionals, this paper presents itself as the first to assess the knowledge levels and application of digital technology by property academics. [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]