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In this short response to the papers appearing in this special issue (Technology, AI Bots and Psychotherapy After Covid), psychotherapist and author Aaron Balick draws on the variety of themes that have arisen within the contributed papers to reflect on the wider issue of computer mediated human relations. In it he makes a distinction between the papers that focus on the therapeutic process mediated by technology and those that look more broadly at the paradigm of therapy practice in this context. Framing technology as a tool, the author pulls together both strands to explore what psychotherapy research may say about the broader issues of societies mediated by technology and how therapeutic research may contribute to these larger social issues. [ABSTRACT FROM AUTHOR]

AbstractOpenAI’s ChatGPT is an advanced large language model AI that has caused both excitement and concern in academia. TurnItIn and similar software programs are widely used in higher education to detect potential plagiarism. However, the extent to which such software can identify papers produced by ChatGPT remains unclear. The current study partially addressed this question by submitting five versions of short essays about criminological topics generated from ChatGPT to the TurnItIn software. Overall, the results indicated that TurnItIn adequately detected that the essays were not original works (mean percent plagiarized score = 31%). The analyses further illustrated that ChatGPT wrote at an exceedingly high level (mean Flesch-Kincaid Grade Level = 15.1) atypical of essays in higher education. Consequently, it appears that detection of AI-generated writing may be easier than initially assumed although the technology is bound to improve. Accordingly, possible strategies for approaching AI in higher education are proposed. [ABSTRACT FROM AUTHOR]

As any field evolves, so do journals' expectations from authors. As Artificial Intelligence (AI) usage in Health Professions Education (HPE) has evolved, Medical Teacher's expectations have changed, and previously-accepted paper types are now routinely rejected. This commentary gives some guidance for authors currently submitting AI in HPE papers to Medical Teacher. [ABSTRACT FROM AUTHOR]

Lawrence Norden, an American attorney and vice president of the Elections & Government Program at the Brennan Center for Justice, discusses the security and integrity of US elections. He identifies cyberthreats, physical threats, and disinformation as the main threats, with both foreign governments and domestic actors posing a risk. Norden emphasizes the progress made in securing election infrastructure, such as the use of paper ballots and post-election audits. He also stresses the importance of information sharing and public education to combat disinformation. Norden mentions the potential impact of artificial intelligence on elections, particularly in spreading misinformation. He concludes by highlighting the need for ongoing investment and updating of election infrastructure to address evolving threats. The article also explores the benefits and challenges of the decentralized election system in the United States, emphasizing the need for minimum security standards across all jurisdictions. The author expresses concern about Project 2025, which proposes to limit the role of the Cybersecurity and Infrastructure Security Agency in assessing cyber hygiene. The article also emphasizes the importance of effective communication between government agencies and social media companies to address foreign interference in elections. Lastly, the author discusses threats to American democracy, including harassment and abuse faced by election officials and the potential weaponization of the Department of Justice against them. Despite these challenges, the article highlights the resilience of democratic institutions and the efforts of individuals to protect democracy and civil society. [Extracted from the article]

With the increasing integration of AI tools like Yewno Discover, Scholarcy, and Grammarly in academic libraries, undergraduate research has witnessed transformative changes. These tools, while elevating the research process, also bring forth challenges rooted in ethics and application. This paper explores the synergy between modern technology and academic exploration, highlighting the benefits and potential pitfalls of using AI in the research workflow. It emphasizes that while Yewno Discover and similar tools offer streamlined navigation of vast information databases, it is imperative for undergraduates to remain cognizant of potential biases and other ethical considerations. This paper underscores the need for proactive measures in academic settings, including specialized training and policy development, to ensure that undergraduate researchers harness the power of AI responsibly and efficiently. [ABSTRACT FROM AUTHOR]

AbstractThe rise of artificial intelligence (AI), represented by ChatGPT, has triggered a new wave of technological competition between nations. Major powers like China and the United States, backed by abundant resources, dominate the discourse and hold a significant advantage in the AI field. However, as the technological rivalry between these two countries intensifies, AI represents a classic ‘double-edged sword’ for many small and medium-sized countries. Therefore, the core argument of this paper is that the AI policies of small and medium-sized countries are not simply about bandwagoning or depending on major powers. At their core, these policies represent a complex form of technological hedging. External security concerns drive small and medium-sized countries to adopt hedging as a primary strategy, while internal interest preferences influence the varying intensities of this technological hedging. The findings of this paper indicate that small countries’ technological policies can be categorized into three models: strong hedging, medium hedging, and weak hedging. Different paths of technological hedging reflect the rational balance these nations make between security and economic interests in different contexts. The case of AI development in ASEAN also demonstrates the significant imbalance between member states. Singapore is classified as a technological frontrunner, while Malaysia, Indonesia, and Vietnam are categorized as followers. Laos, Cambodia, Myanmar, Thailand, Brunei, and the Philippines are considered latecomers in the field of AI. In terms of specific AI policies, Singapore and Vietnam adopt a strong technological hedging strategy, seeking more cooperation and interaction with the U.S. or other countries due to caution and concerns about China’s technological security. Malaysia, Indonesia, Thailand, and the Philippines follow a medium hedging strategy, while Cambodia, Laos, Myanmar, and Brunei adopt a weak hedging approach. The differences in hedging intensity are primarily due to varying internal legitimacy pathways and the strength of backup resources. ASEAN member states pursue different technological paths based on their own circumstances to promote AI development. [ABSTRACT FROM AUTHOR]

With the popularisation of the concept of sustainable traditional wickerwork, wickerwork cultural and creative products have become the most popular craft production. Kansei consensus on the design of wickerwork lamps has become a key factor influencing the communication between manufacturers and customers. Therefore, the purpose of this paper is to develop a product evaluation model for wickerwork cultural and creative products that meet the emotional satisfaction of users. Firstly, this paper could build a three-level evaluation grid diagram driven by user attractiveness through EGM, and extracts key dimensions in the perceptual vocabulary using FA. Secondly, evaluation indicators are constructed and grey relation analysis (GRA) is used to synthetically assess the priority order of evaluation indicators based on the obtained weight values. Finally, in order to effectively deal with uncertain product evaluation information, the intuitionistic fuzzy VIKOR method was used to evaluate representative product design solutions, and the most perceptually attractive product design solution was preferentially selected. This study selected wickerwork lamps as the design case of cultural and creative products, the results of the study enable designers to accurately grasp customers’ emotional perceptions of wickerwork products and obtain the best wickerwork production design solution. [ABSTRACT FROM AUTHOR]

This is a critique of how designers deal with temporality in design to speculate about socio-technical futures. The paper unpacks how embedded definitions and assumptions of temporality in current design tools contribute to coloniality in designed futures. Based on this critique, we reject the notion that it is only AI that needs fixing, as design practice becomes implicated in how oppression extends from physical systems to global digital platforms. To make these issues visible, we dissect the Futures Cone model used in speculative design. As an alternative, the paper then presents hauntology as a vocabulary that can aid designers in accommodating pluriversal histories in anticipatory futures and reorienting their speculative tools. To illustrate the benefits of the proposed metaphors, the paper highlights examples of coloniality in digital spaces and emphasizes the failure of speculative design to decolonize future imaginaries. Using points of reference from hauntology, ones that engage with states of lingering or spectrality, and notions of nostalgia, absence, and anticipation, the paper contributes to rethinking the role that design tools play in colonizing future imaginaries, especially those pertaining to potentially disruptive technologies. [ABSTRACT FROM AUTHOR]

AbstractThis Systematic Literature Review explores the evolving landscape of consumer behaviour towards DVAs using the TCCM (Theory-Context-Characteristics-Methodology) framework. Through a comprehensive analysis of 64 research papers sourced from the Scopus database, indexed in the ABDC (Australian Business Deans Council) category list, and published until December 2023, we uncover pivotal themes and significant knowledge gaps in the current body of research. The findings show that most studies on consumer-DVA interactions are empirical in nature, predominantly focused on the shopping and hospitality sectors, and grounded in the Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) frameworks. Applying the TCCM framework, this paper uncovers fertile ground for future research, highlighting opportunities for further exploration and development. Building on the identified research gaps and our insights, we present integrated frameworks on consumer behaviour towards DVAs. Additionally, we provide actionable recommendations for DVA researchers, developers, and marketers, highlighting critical areas for future research, such as integrating sentiment analysis and emotional recognition capabilities. We also propose tailored strategies, including customizable DVA personalities and engaging voice experiences, to enhance consumer engagement and brand loyalty. The paper concludes with a summary of limitations and key findings. [ABSTRACT FROM AUTHOR]

When it comes to relationships with technology, questions of trust and trustworthiness are never far away. This paper explores how the rapid expansion of Artificial Intelligence (AI) technologies into people’s everyday lives pushes the question of what it means to trust technology into new and unfamiliar territories, far beyond traditional frameworks associated with either functional reliability (subject-object), or those that take their inspiration from interpersonal (subject-subject) relations. Challenging the cognitivist and humanist emphases of such models of trust, the paper instead develops a more ontological sense of trust which foregrounds the complex material processes and unconscious forces that shape how people think and relate to AI. Drawing on in-depth interviews with users of AI-enabled navigation apps (like Google Maps and Waze), we draw out these ontological dimensions of trust in three main ways. First, how relationships with these technologies are strongly shaped by imaginaries that have significant performative impacts on how AI is conceived and whether and how it can be trusted. Second, how people’s sense of trust is often attuned to the broader socio-technical ecologies that shape AI’s existence. And finally, how the affective force of everyday habits enables or constrains trust in relation to specific contexts and scenarios. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) can significantly contribute to the implementation of the United Nations Sustainable Development Goals (SDGs) by offering innovative solutions and enhancing the efficiency of processes aimed at achieving these goals. There is a perceived need for studies which may look at these connections. Against this background, this paper reports on a study that investigated the connections between artificial intelligence and the implementation of the UN Sustainable Development Goals (SDGs) at higher education institutions. The paper deployed a multi-methods approach. The first one was a bibliometric analysis of publications in the topic. The second method used was an assessment of a set of case studies, that illustrate how artificial intelligence is being deployed among a sample of universities in support of efforts to implement the SDGs and a survey aimed at identifying current and future trends. The data gathered allow some trends to be identified. For instance, that there is a wide range of applications of AI to sustainability in High Education Institutions (HEI), to be chosen in terms of campus operations and greening, outreach and community engagement, research, teaching and learning, and university management. Also, the paper has identified successful examples of the deployment of AI in various sustainability contexts, illustrating what are the success factors for them. Moreover, the survey identified the fact that the use of AI is quite widely spread, and is likely to increase in coming years, due to a greater demand. Finally, AI also poses several challenges, such as authenticity and ethics in assessment (case studies), 'lack of access to software/materials', and 'lack of information technology training for myself/my colleagues' (survey). Overall, AI offers a powerful toolset to accelerate and enhance the implementation of the UN SDGs. By analysing vast datasets, predicting outcomes, optimising processes, and providing new insights, AI has the potential to address complex sustainability challenges across various sectors. HIGHLIGHTS: Artificial intelligence (AI) is fast becoming a component of modern life, being used in many areas. AI has a growing impact on achieving the Sustainable Development Goals. It can catalyse innovations in areas as varied as circular economy and smart cities. AI offers a vital nexus between sustainable development and effective climate change adaptation. [ABSTRACT FROM AUTHOR]

Several emerging technologies afford a specific type of interference with privacy which has not yet been conceptualised: inferring information about mental properties of persons on the basis of physiological or biometric signals, in short: mind probing. This paper conceptualises it, analyses several forms and proposes concrete norms that ban and criminalise it. Facial emotion recognition, neuroimaging, or wearable consumer devices such as smartwatches process biometric data to draw inferences about minds; the prevalence and sophistication of such mind probes will soon increase through AI data analysis. This paper explores how human rights law, the EU General Data Protection Regulation and the EU AI Act address mind probes and shows why the current level of protection appears insufficient. This motivates novel norms banning and criminalising non-consensual biometric-based forms of mind reading, ensuring that at least some parts of the mind remain in principle free from non-consensual mind probing. [ABSTRACT FROM AUTHOR]

AI-enabled assessment of student papers has the potential to provide both summative and formative feedback and reduce the time spent on grading. Using auto-ethnography, this study compares AI-enabled and human assessment of business student examination papers in a law module based on previously established rubrics. Examination papers were corrected by the professor and then subjected to a series of tests by Gen-AI tools. While we were impressed with the personalised feedback of Gen-AI tools, the accuracy of grading and the learning capacity of AI tools, we found that Gen-AI tools used are not fully satisfactory to enable fully autonomous correction due to erroneous grading, the hallucination phenomenon and verbose feedback that is not always personalised. The 8C model of challenges of AI-enabled correction is outlined. This paper has implications for professors, HEIs and instructional designers and all those who correct student papers in a third-level institution. [ABSTRACT FROM AUTHOR]

Prediction of heart diseases on time is significant in order to preserve life. Many conventional methods have taken efforts on earlier prediction but faced with challenges of higher prediction cost, extended time for computation and complexities with larger volume of data which reduced prediction accuracy. In order to overcome such pitfalls, AI (Artificial Intelligence) technology has been evolved in diagnosing heart diseases through deployment of several ML (Machine Learning) and DL (Deep Learning) algorithms. It improves detection by influencing with its capacity of learning from the massive data containing age, obesity, hypertension and other risk factors of patients and extract it accordingly to differentiate on the circumstances. Moreover, storage of larger data with AI greatly assists in analysing the occurrence of the disease from past historical data. Hence, this paper intends to provide a review on different AI based algorithms used in the heart disease prognostication and delivers its benefits through researching on various existing works. It performs comparative analysis and critical assessment as encompassing accuracies and maximum utilization of algorithms focussed by traditional studies in this area. The major findings of the paper emphasized on the evolution and continuous explorations of AI techniques for heart disease prediction and the future researchers aims in determining the dimensions that have attained high and low prediction accuracies on which appropriate research works can be performed. Finally, future research is included to offer new stimulus for further investigation of AI in cardiac disease diagnosis. [ABSTRACT FROM AUTHOR]

This paper investigates the contributions of Pope Francis toward human-centered AI and digital innovation. It draws from his numerous dialogues with experts in technology, medicine, science, ethics, law, philosophy, and theology. The papal engagements are based on the rich patrimony of Catholic Social Teaching that is being updated in the wake of the digital revolutions. The paper explores the Magisterium of Francis with the question of technological progress and inherent dangers for human society. It underscores some fundamental anthropological and ethical themes that the Pontiff considered necessary in making emerging technologies beneficial. [ABSTRACT FROM AUTHOR]

In the wake of the pervasive digitalisation of industry, the disruption of global supply chains during the COVID-19 pandemic, and a geopolitical race for the development of artificial intelligence models, the European Union has pivoted towards promoting 'digital sovereignty' across the technological stack. The paper looks more specifically at the cloud layer and analyses the draft European Union Cloud Services Scheme (EUCS) launched by the European Union Agency for Cybersecurity in December 2020. While EUCS is a highly technical scheme, the attempt to include digital sovereignty provisions in it provoked unexpected controversies around who pushes for 'digital sovereignty', why, and how feasible it is. The paper argues, first, that distributive conflicts between member states have led to strong objections towards including digital sovereignty provisions in EUCS. Second, diverging national preferences have also played out in horizontal inter-institutional conflicts around competences between the European Commission and the European Parliament. All in all, the paper makes a novel empirical contribution by studying the overlooked case of EUCS. Theoretically, it bridges the growing literature on digital sovereignty with classic theories of EU integration to identify key factors hindering the translation of digital sovereignty discourses into policy within a broader context of limited technological capabilities. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) combined with image processing has shown significant improvements through new techniques such as Machine Learning (ML) models. This paper introduces the key methods and algorithms used for Drone image processing. We discuss the benefits and limitations of using ML models instead of classical techniques. Our goal is to classify, categorize and describe the methods that are used in realistic settings of diverse domains of applications. We conducted a systematic literature review where systems presented in the papers were analysed based on their domain, task, technology, and efficiency. By extensively reviewing the existing literature, we successfully identified key themes and trends that emerged across the various research questions. The overall findings of the research emphasise the potential of AI and drone imagery in numerous fields. However, the review also uncovered several challenges that necessitate attention, such as issues related to data quality and the requirement for more advanced AI algorithms. The paper outlines significant innovations in the field and offers recommendations for future research directions. By highlighting cross-disciplinary insights, it delves into methodological approaches, exploring commonalities in AI algorithms and UAVs technologies. [ABSTRACT FROM AUTHOR]

The paper explores how Generative AI intersects with Competition Law, focusing on Foundation Models (FMs) and Large Language Models (LLMs). It examines industry dynamics and identifies key competition issues like entry barriers, tying, leveraging, and acquisitions. It highlights the supply chain's importance and looks at how FMs are integrated into search software, chatbots, and productivity tools, particularly noting entry barriers such as computing power and data collection. It suggests that FMs might require new approaches to market delineation, possibly creating a separate relevant market for data. The paper also discusses various cases pertaining to tying and leveraging and highlights the difficulty in proving tying due to the blurred lines between traditional search engines and AI chatbots. It illustrates how competition assessments for acquisitions may require changes due to data being a highly flexible commodity for the industry. The paper concludes by calling for increased scrutiny and regulation for the industry. [ABSTRACT FROM AUTHOR]

AbstractSituational awareness and decision support tools such as procedures and alarm systems are vital for effective interaction among control room operators, especially in safety-critical situations. In safety-critical environments such as process plants, there remains a gap in evaluating specific tools during actual operations, or ”work-as-done.” Additionally, the underlying factors that might impact operators’ cognitive states and performance concerning safety have not been thoroughly explored. The need for such an evaluation is further bolstered by current interaction configurations where operators are more passive than active, thus reducing their cognitive performance. Therefore, this experimental study addresses the highlighted evaluation gap by introducing and comparing three human system interfaces/decision support tools in four human-in-the-loop configurations. The supports include two alarm design formats (prioritized vs. non-prioritized) and three procedure representation formats (paper, screen-based digitized, and an AI-based support system built with an integrated Bayesian network and reinforcement learning model). Ninety-two people (n = 92) participated voluntarily in the test. They were divided equally into four groups. Each group tested three safety-related events in a simulated formaldehyde production facility. Individuals belonging to the group with prioritized alarms and utilized paper procedures rated procedural support slightly higher on average than others in different groups. Unlike the other groups, their assessment of alarm prioritization support remained consistent across all scenarios. Further analysis of the impact of the setup on cognitive states and actual performance will be performed. [ABSTRACT FROM AUTHOR]

Police departments around the world implement algorithmic systems to enhance various policing tasks. Ensuring such innovations take place responsibly – with public values upheld – is essential for public organisations. This paper analyses how public values are safeguarded in the case of MONOcam, an algorithmic camera system designed and used by the Netherlands police. The system employs artificial intelligence to detect whether car drivers are holding a mobile device. MONOcam can be considered a good example of value-sensitive design; many measures were taken to safeguard public values in this algorithmic system. In pursuit of responsible implementation of algorithms, most calls and literature focus on such value-sensitive design. Less attention is paid to what happens beyond design. Building on 120+ hours of ethnographic observations as well as informal conversations and three semi-structured interviews, this research shows that public values deemed safeguarded in design are re-negotiated as the system is implemented and used in practice. These findings led to direct impact, as MONOcam was improved in response. This paper thus highlights that algorithmic system design is often based on an ideal world, but it is in the complexities and fuzzy realities of everyday professional routines and sociomaterial reality that these systems are enacted, and public values are renegotiated in the use of algorithms. While value-sensitive design is important, this paper shows that it offers no guarantees for safeguarding public values in practice. [ABSTRACT FROM AUTHOR]

While artificial intelligence technologies are increasingly studied as drivers of "digital authoritarianism," resistance to this process has remained underexplored. Our paper addresses this gap by asking why and how citizens resist AI-powered autocratization in consolidated democracies. We first conceptualize this resistance as distinct from other forms of anti-autocratic resistance in that it is anticipatory rather than reactive, and directed at existing authorities rather than new democratic challengers. We then introduce techno-authoritarian imaginaries as a novel concept to understand the drivers and shapes of this resistance. First, we argue that activists from civil society draw on these broader, pre-existing imaginaries of authoritarian futures to make sense of new technologies and articulate technology-specific problem frames. Second, we propose that imaginaries are contingent on historical and political experiences and therefore differ between contexts. Such differences, in turn, shape how the respective targets respond to resistance. We illustrate our argument by case studies of campaigns against facial recognition technology in the U.S. and the European Union. Our paper enriches existing debates on resistance to autocratization and advocates or a more pronounced engagement with practices of future-making, as constructions of societal futures—both desirable and undesirable—are becoming an increasingly important source for democratic mobilization. [ABSTRACT FROM AUTHOR]

This paper explores how regulatory sandboxes can be used to promote the development of trustworthy artificial intelligence (AI) systems. The analysis focus on the experiences of Data Protection Authorities (DPAs) who have been experimenting with sandboxes to foster the implementation of privacy by design principles in AI systems. Throughout bibliographic research, this study (i) highlights privacy-related elements on international AI regulatory frameworks; (ii) explores the concept of privacy by design (PbD) and some of its strategies, patterns and techniques, such as Privacy Enhancing Technologies (PETs); (iii) reflects upon the impact of regulatory sandboxes; and (iv) analyses how regulatory sandboxes are being discussed in the context of AI regulatory frameworks. While this study brings global perspective on regulatory sandboxes, it also does relevant analysis in the Latin American context by presenting different stages of development of initiatives in the region. One of the main findings of this paper is that literature currently lacks an analysis of how privacy by design could be used in the context of AI systems design and governance. It also suggests that regulatory sandboxes may be able to integrate PbD principles to foster the development of trustworthy AI, although further research would be needed on that matter. [ABSTRACT FROM AUTHOR]

The United Nations Educational, Scientific and Cultural Organization (UNESCO) launched its Futures of Education policy initiative in early 2020. The process sought to open a sustained global policy debate on educational futures in a world typified by climatological instability, social inequity, and political unrest. Drawing on Deleuze and Guattari’s theory of assemblages, this paper explores how the Futures of Education initiative simultaneously produces, captures, and reroutes flows of desire by opening its policymaking process to global consultation. Following this line of thinking, the paper introduces a speculative method for critical policy analysis that utilises AI-powered tools to generate expressive images and imaginaries of education futures based on UNESCO’s three stages of policy development. The authors argue that this method enables a step beyond conventional interpretations of a policy’s discursive content toward a speculative analysis of its processual dynamics and expressive potentials. Ultimately, the paper underscores the generative possibilities of AI-assisted methods for tracking flows and expressions of desire within education policy assemblages under conditions of climatological and political upheaval. The authors encourage further experimentation with plugging emerging technologies into global policy assemblages to better understand how desire is invested into particular images and imaginaries of educational futures. [ABSTRACT FROM AUTHOR]

Learning around artificial intelligence (AI)-powered technologies that attends to power is an urgent and widely felt priority among the learning sciences and CS ed broadly. Popular approaches to AI education focus on technical skills, with far less theoretical and practical work around critical and justice-centered AI learning. Adding to this literature, we discuss tool design and observed interactions in Drag vs AI workshops, where participants use hands-on makeup art as a medium for fooling, subverting, and refusing facial recognition. Our broader analysis asks how participants make sense of the technical and political aspects of AI, as they interact with AI through the Drag vs AI workshops' modes of aesthetic transformation, tinkering, and resistance. In this paper, we focus on participants' embodied algorithmic tinkering with AI and affordances for justice-centered computing education. Our analysis highlights how tinkering and play modes of interaction with AI materials can promote critical and agentive learning. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and its data-driven counterpart, machine learning (ML), are rapidly evolving disciplines with increasing applications in modeling, simulation, control, and optimization within the drying industry. This paper presents a comprehensive overview of progress made in ML from shallow to deep learning and its implications for food drying. Theoretical foundations, advantages, and limitations of various ML approaches employed in this domain are explored. Additionally, advancements in ML models, particularly those enhanced by optimization algorithms, are reviewed. The review underscores the role of intelligent configuration of ML models, which affects their accuracy and ability to solve problems of high energy consumption, nutrient degradation, and uneven drying. Drawing upon research achievements, integrating of AI models with real-time measuring methods is discussed, enabling dynamic determination of optimal drying conditions and parameter adjustments. This integration facilitates automated decision-making, reducing human errors and enhancing operational efficiency in food drying. Moreover, AI models demonstrate proficiency in predicting drying times and analyzing energy usage patterns, thereby enabling optimization to minimize resource consumption while preserving product quality. Finally, this paper identifies current obstacles in technology development and proposes novel research avenues for sustainable drying technologies. The strengths and weaknesses of various AI methodologies are examined Artificial neural networks are extensively used for modeling drying phenomena Machine learning models can simulate complex processes of food drying Deep learning has significant potential for real-time monitoring of drying Intelligent control systems can optimize food drying [ABSTRACT FROM AUTHOR]

This discussion article examines the potential integration of Generative Artificial Intelligence (Gen-AI), including advanced Large-Language Models like the popular platform ChatGPT into subject English education. Following the significant public and academic attention in response to these technologies through 2023, this paper considers the transformative potential and challenges posed by Gen-AI in educational settings. Central to the discussion is the exploration of how English teachers can leverage Gen-AI to enrich student learning beyond the obvious domain of writing skills. Instead, the article foregrounds the necessity for students' understanding of Gen-AI as an essential component of digital literacy. While acknowledging ethical concerns such as plagiarism, equity, and access, the paper presents an argument for the productive use of Gen-AI in the classroom to augment reading, viewing, and interpretation lessons. Avoiding an evangelical or dystopian view of AI, this discussion piece explores the time-critical and urgent issue of how, when, and why English can engage with the technology. [ABSTRACT FROM AUTHOR]

This paper examines how AI ethics has been developed at the national level in Japan, and what this process reveals about broader Japanese state imaginaries of how advanced technology should be developed and used, and what a future with these technologies should look like. Key developments in the Japanese government's approach to AI ethics and governance between 2014 and 2023 are laid out, based on an analysis of official reports and policy documents supplemented by data collected via semi-structured interviews with three expert members of the committees that formulated several key sets of ethical principles. The paper considers Japan's positioning in the global race to develop AI ethics principles over this period, as well as the imaginary of AI within the wider historical context of imaginaries about the knowledge society in Japan. I suggest three ways in which AI ethics has been understood and instrumentalized in the Japanese context, and argue that the main methodology used to date—ELSI—complements the government's utopian and techno-determinist imaginaries of the future while concealing a deeply conservative approach that serves to reproduce structural inequalities and discrimination despite the apparent internationalism and progressive values that are repeatedly expressed in state-promoted ethical principles. [ABSTRACT FROM AUTHOR]

Creativity has long been viewed as the bastion of human expression. With the advent of generative artificial intelligence (AI), there is an emerging notion of artificial creativity that contests traditional perspectives of artistic exploration. This paper explores the complex dynamics of this evolution by examining how generative AI intertwines with and transforms the art world. It presents a comprehensive analysis of the challenges posed by generative AI in art, from questions of authenticity and intellectual property to ethical dilemmas and impacts on conventional art practices. Simultaneously, it investigates the revolutionary opportunities generative AI offers, including the democratization of art creation, the expansion of creative boundaries, and the development of new collaborative and economic models. The paper posits that the integration of generative AI in art is not just a technological advancement but a significant cultural shift, which necessitates a reevaluation of our understanding of art and the artist. It concludes with a forward-looking perspective, advocating for a collaborative approach to harness the potential of this technology in enriching human creativity and ensuring the vibrant evolution of the art world in the era of AI-driven generation. [ABSTRACT FROM AUTHOR]

Various algorithms have been put forth for an efficiency estimation of an in-service induction motor with varying intrusion levels and accuracy. In the field, it is hard to reach the terminals of an in-service motor because of its location. This paper helps a field engineer to choose properly an efficiency estimation method for condition monitoring of an in-service induction motor. In this paper, the author put forth an approach based on symmetrical components to assess the efficiency of induction machine working on unbalanced power supplies at any loading condition. A comparison is done between two offline intelligence techniques like gravitational search (GS) algorithm and cuckoo search (CS) algorithm for estimating the efficiency of an induction machine by using limited measurements. The merit of these techniques is that the motor is not taken out from its place of installation for testing. Also, the highly intrusive no-load test is avoided. Simulations and experimental results support and validate the carried-out research. [ABSTRACT FROM AUTHOR]

The lack of explainability of Artificial Intelligence (AI) is one of the first obstacles that the industry and regulators must overcome to mitigate the risks associated with the technology. The need for 'eXplainable AI' (XAI) is evident in fields where accountability, ethics and fairness are critical, such as healthcare, credit scoring, policing and the criminal justice system. At the EU level, the notion of explainability is one of the fundamental principles that underpin the AI Act, though the exact XAI techniques and requirements are still to be determined and tested in practice. This paper explores various approaches and techniques that promise to advance XAI, as well as the challenges of implementing the principle of explainability in AI governance and policies. Finally, the paper examines the integration of XAI into EU law, emphasising the issues of standard setting, oversight, and enforcement. [ABSTRACT FROM AUTHOR]

In recent years, Game-Based Learning (GBL) has been widely adopted in various educational settings. This paper aims to review empirical studies that adopt GBL in the field of AI education and explore its future research perspectives. After a systematic keyword search in the online database and a snowballing approach, a total of 125 empirical papers with 133 studies were targeted as samples. Results indicated that the games in AI education are mainly fell into five categories: Puzzle games are the most used in the curriculum (27.07%), followed by Reasoning strategy games (23.31%), Robot games (18.05%), Role-playing games (9.02%) and Simulation games (6.77%). Among them, 22.39% of games were with real characters, 11.94% were with virtual characters and 64.18% were with no characters. Besides, games were used in three main forms in AI education: games as teaching tools (78.95%), games as student works (12.03%), and games as a competing mechanism (9.02%). Researchers mainly paid attention to the effect of GBL on students' Opinions and Attitude (52.96%) and Learning achievement (24.04%), while the other three categories such as Skills and ability, Interaction, and Cognition were not extensively measured. The cross-sectional analysis, research gaps, and potential directions for future research were also discussed. [ABSTRACT FROM AUTHOR]

Purpose: Numerous uveitis articles were published in this century, underneath which hides valuable intelligence. We aimed to characterize the evolution and patterns in this field. Methods: We divided the 15,994 uveitis papers into four consecutive time periods for bibliometric analysis, and applied latent Dirichlet allocation topic modeling and machine learning techniques to the latest period. Results: The yearly publication pattern fitted the curve: 1.21335x2 − 4,848.95282x + 4,844,935.58876 (R2 = 0.98311). The USA, the most productive country/region, focused on topics like ankylosing spondylitis and biologic therapy, whereas China (mainland) focused on topics like OCT and Behcet disease. The logistic regression showed the highest accuracy (71.6%) in the test set. Conclusion: In this century, a growing number of countries/regions/authors/journals are involved in the uveitis study, promoting the scientific output and thematic evolution. Our pioneering study uncovers the evolving academic trends and frontier patterns in this field using bibliometric analysis and AI algorithms. [ABSTRACT FROM AUTHOR]

The present study is done to perform the optimal design of the structural components of the buildings against the unwanted wind load exerted on their outer face. To this end, the case study of the research is the outer wall made of a one-floor building modeled as a rectangular plate with only one free edge and three clamped ones. It is assumed that the wall is a sandwich plate whose core is made of auxetic material and dace-sheets are reinforced with nanoparticles of graphene platelets (GPL). Differential equations governing the system's motion are obtained within the background of the plate's shear-deformation theories. The stability analysis of the sandwich wall is performed based on the application of artificial intelligence (AI) methods optimized with an innovative optimization approach to gain a high level of accuracy. To determine the stability information of the system at the train points, the differential quadrature approach (DQA) is applied as the solver of differential equations of motion. The accuracy of the methods used in this paper is examined and verified by comparing the results with those acquired in the articles published previously. The results obtained in this study provide very useful information about the stability response of lightweight building components through AI-based solutions. [ABSTRACT FROM AUTHOR]

AbstractThe paper explores the synergy between wearable data and augmented Artificial Intelligence (AI) through findings from two interconnected studies. The first study (study 1) focuses on medical professionals’ perceptions of wearable data and AI, and the second study (Study 2) extends it focuses on how differences in the level of granularity in the data presented affect the professionals’ understanding, interpretation, and trust in AI recommendations. This system allows medical professionals to view AI-generated recommendations for sleep and activity improvement and explanations of the underlying rationale. While each study has distinct research questions, Study 2 builds upon Study 1's foundation. Both studies employed scenario-based interviews. Thematic analysis of Study 1 identified trust as a crucial factor in the acceptance of wearable data and AI, influencing Study 2's exploration of factors affecting trust, such as explainability, data granularity, representativeness, and user interaction. Study 2 highlighted varying perspectives on information sufficiency and data sharing from the AI system linked to professionals’ roles and tasks. The work offers insights into data granularity’s impact on engagement with AI recommendations. [ABSTRACT FROM AUTHOR]

In the context of intelligent systems for human-vehicle collaboration, the fusion of information space, physical space, and user cognitive space has become a trend. This paper aims to address the challenges posed by information perception gaps and cognitive limitations experienced by drivers by leveraging Augmented Reality Head-up Displays (ie, AR-HUD) to compensate for perceptual deficiencies and enhance driver cognition. We introduce the innovative concept of the Human-Machine Team Situation Awareness (ie, TSA) loop model. Firstly, we analyze the cognitive characteristics of drivers and the spatiotemporal information elements within hazardous scenarios. Subsequently, AR-HUDs are employed to provide drivers with perceptual compensation and cognitive enhancement. Furthermore, we design AR-HUD interfaces for two representative scenarios. The results demonstrate that, with the support of AR-HUDs, the integration of dynamic interface elements proves to be more effective in compensating for perceptual deficiencies, and the inclusion of predictive information contributes to improved driving performance. Notably, in emergency situations, AR-HUDs play a crucial role in providing decision-enhancing information to drivers. The proposed theoretical framework offers opportunities for expanding the theoretical approaches and application domains of AR-HUDs. [ABSTRACT FROM AUTHOR]

Automated employment decision tools use machine learning, artificial intelligence, predictive analytics, and other data-driven approaches to enhance candidate experiences and streamline employment related decision-making, allowing human resources to be concentrated where they are needed most. However, the use of these tools without appropriate safeguards has resulted in a number of high-profile scandals in recent years, particularly in regard to bias. Accordingly, lawmakers have started to propose laws that require bias audits of automated employment decision tools to examine their outputs for subgroup differences. The first of its kind was New York City Local Law 144, but other US states have since followed suit. In this paper, we examine the concerns about the effectiveness of this and other similar laws, including the suitability of metrics, the scope of the law, and low levels of compliance. We conclude that despite the law being a good initial first step towards greater transparency around automated employment decision tools and reducing bias, examining outcomes alone is not sufficient to prevent bias elsewhere in the tool. Moreover, effective bias prevention will require a multidisciplinary approach that combines expertise in IO psychology, law, and computer science to develop appropriate metrics and maximize the enforceability of such laws. [ABSTRACT FROM AUTHOR]

AbstractIntelligent environments are rapidly gaining ground, propelled by a rich sensor infrastructure, the Internet of Things, sophisticated reasoning capabilities, and Artificial Intelligence. In this complex technological landscape, crafting usable intelligent environments and assessing the user experience (UX) demands a thorough understanding of the concepts involved and the parameters that need to be studied. This paper carries out a review of usability and UX evaluation methods and frameworks, elaborating on fundamental concepts and presenting in detail approaches and methods reported in the literature. It additionally examines evaluation approaches in adaptive and ubiquitous computing systems, which are closely associated with intelligent environments, and presents UX challenges and evaluation frameworks in intelligent environments. Finally, the findings are synthesized and consolidated to produce a comprehensive overview of the field and the challenges that lie ahead. [ABSTRACT FROM AUTHOR]

Conversational agents (CA) are thought to be promising for psychotherapy because they give the impression of being able to engage in conversations with human users. However, given the high risk for therapy patients who are already in a vulnerable situation, there is a need to investigate the extent to which CA are able to contribute to therapy goals and to discuss CA’s limitations, especially in complex cases. In this paper, we understand psychotherapy as a way of dealing with existential situations and position CAs in the context of the therapeutic experience of patients. This experience is determined by the patient’s unique personal context and specific therapy goals. We suggest that psychotherapy is a fundamentally dialogical activity, because it crucially involves work on the self and one’s self-narrative. This brings us to our central question: is it possible for CAs to engage in a productive therapeutic dialogue, given their limitations as epistemic agents? We will discuss several of those limitations, show how these undermine the possibility of engaging in a therapeutic dialogue, and illustrate those limitations through discussions of the cases of grief and abuse. [ABSTRACT FROM AUTHOR]

AbstractIn complex industrial and chemical process control rooms, effective decision-making is crucial for safety and efficiency. The experiments in this paper evaluate the impact and applications of an AI-based decision support system integrated into an improved human-machine interface, using dynamic influence diagrams, a hidden Markov model, and deep reinforcement learning. The enhanced support system aims to reduce operator workload, improve situational awareness, and provide different intervention strategies to the operator adapted to the current state of both the system and human performance. Such a system can be particularly useful in cases of information overload when many alarms and inputs are presented all within the same time window, or for junior operators during training. A comprehensive cross-data analysis was conducted, involving 47 participants and a diverse range of data sources such as smartwatch metrics, eye-tracking data, process logs, and responses from questionnaires. The results indicate interesting insights regarding the effectiveness of the approach in aiding decision-making, decreasing perceived workload, and increasing situational awareness for the scenarios considered. Additionally, the results provide insights to compare differences between styles of information gathering when using the system by individual participants. These findings are particularly relevant when predicting the overall performance of the individual participant and their capacity to successfully handle a plant upset and the alarms connected to it using process and human-machine interaction logs in real-time which resulted in a 95.8% prediction accuracy using hidden Markov model. These predictions enable the development of more effective intervention strategies. [ABSTRACT FROM AUTHOR]

Learners' emotional states might change during the learning process, and unpredictable variations of a person's emotions raise the demand for regular assessment of feelings during learning. In this paper, an AI-based decision framework is proposed and implemented for e-learning systems that identify suitable micro-brake activities based on the learner's emotional state through an evolutionary genetic algorithm to change learner's mood and increase learning performance. This proposed framework was tested using a case study of English as a second language learner during one semester. The students were divided into two groups of participants (each group containing twenty students, forming a total of 40 students). The results of this study demonstrated the importance of learners' emotions in their learning performance and proved the effectiveness of our proposed framework and the success of the recommended micro-break activities chosen based on learners' emotions and preferences. The findings of this study have important practical implications in designing adaptive e-learning systems and learning management systems such as Moodle. They also contribute to theoretical implications in the field of AI and learner emotions by suggesting a novel approach to identifying, categorizing, and offering a learning path that can cater to the needs of individual learners. [ABSTRACT FROM AUTHOR]

With the emergence of the Industrial Revolution 4.0, modern technologies such as cloud computing, artificial intelligence, and big data are profoundly transforming the education ecosystem. The development of education is not only faced with huge challenges but also contains rare opportunities. New concepts such as deep learning, adaptive learning, and blended learning do not break through the inherent barriers of learning theory, and they also expedite the restoration of the educational technology ecosystem. Based on the ASSURE model, this paper systematically discusses the new development concept of educational technology. Heinich, Russell, and Molenda proposed the ASSURE model in 1989. The "A" stands for Analyze Learner, the first "S" is assumed as Objectives and State Standards; the second "S" for Selecting Media, Strategies, Materials, and Technology; the "U" for Utilizing Materials, Media, and Technology; the "R" stands for Required Learner Participation; and the "E" is assigned for Revise and Evaluate. It is widely used in classroom instruction, online learning, and organizational training. Educational technology must embrace new technology, renew the concept of educational development, shape the new pattern of educational ecology, and better serve the needs of cultivating innovative talents. [ABSTRACT FROM AUTHOR]

This document is an editorial from the journal "Cartography & Geographic Information Science" that recognizes the contributions of Professor Emerita Barbara P. Buttenfield to the field of Cartography and Geographic Information Science (GIScience). It highlights her research areas, academic honors, and awards. The editorial also discusses a scientometric analysis of papers citing Buttenfield's work, revealing four main areas of impact: visualization, mapping and GIS, population dynamics and urban issues, and environmental focus. The document introduces the five contributions to the special issue, which align with Buttenfield's main areas of impact. The guest editors express their gratitude to the authors and reviewers involved in the special issue and wish Buttenfield a happy retirement. [Extracted from the article]

Layout designs have a large number of design variables and various physical constraints. Conventional design exploration approaches are time-consuming and may require human intervention. A unique feature about physical layout designs is the availability of domain knowledge, which can be utilised to speed up the design process. In this paper, we propose a generative deep learning-based design space exploration (DSE) methodology that is capable of learning design constraints in the layout design without explicit supervision. Moreover, it can incorporate domain knowledge in the generated layouts, thereby speeding up the design process. This is realised by constructing a layout generation variational autoencoder (LGVAE) model, which uses a latent space as an interface to generate the layouts. By training the LGVAE model, significantly lower-dimensional representations can be learned compared to the original dimensionality of the design space. Therefore, the number of design variables is greatly reduced. We showcase the performance of the proposed DSE approach by solving the heat source layout design problem encountered in thermal management of chips. Experiments demonstrate that the LGVAE model is capable of generating compressed latent representations that capture the characteristics of the input samples, which makes the DSE cost-effective. [ABSTRACT FROM AUTHOR]

With the increasing application of Natural Language Processing (NLP) in Medicine at large, medical educators are urged to gain an understanding and implement NLP techniques within their own education programs to improve the workflow and make significant and rapid improvements in their programs. This paper aims to provide twelve essential tips inclusive of both conceptual and technical factors to facilitate the successful integration of NLP in medical education program evaluation. These twelve tips range from advising on various stages of planning the evaluation process, considerations for data collection, and reflections on preprocessing of data in preparation for analysis and interpretation of results. Using these twelve tips as a framework, medical researchers, educators, and administrators will have an understanding and reference to navigating applications of NLP and be able to unlock its potential for enhancing the evaluation of their own medical education programs. [ABSTRACT FROM AUTHOR]

Advances in Artificial Intelligence (AI) have led to AI systems' being used increasingly in medical education research. Current methods of reporting on the research, however, tend to follow patterns of describing an intervention and reporting on results, with little description of the AI in the system, or the many concerns about the use of AI. In essence, the readers do not actually know anything about the system itself. This paper proposes a checklist for reporting on AI systems, and covers the initial protocols and scoping, modelling and code, algorithm design, training data, testing and validation, usage, comparisons, real-world requirements, results and limitations, and ethical considerations. The aim is to have a systematic reporting process so that readers can have a comprehensive understanding of the AI system that was used in the research. [ABSTRACT FROM AUTHOR]

What kind of writing, criticism and photography theory is needed today? Photographies, since 2008 the leading UK-based theory journal, includes research articles, practice-led photo-essays, and critical debates. Journal editors curate diverse articles to cluster papers that inter-relate. Yet readers search online for single articles or named authors. Should we envisage a future within which editors are redundant? In addition, use of artificial intelligence in partially or entirely generating submissions begs questions relating to anonymous peer review principles but may also herald experiments in generative practices, perhaps in the form of 'live' authorial content synthesized with AI contributions. [ABSTRACT FROM AUTHOR]

Research in the field of History of Education has experienced a remarkable increase in recent decades. Resulting publications are referenced in generalist databases that do not catalogue academic works according to the specific characteristics of History of Education. Seeking to give response to this bibliographic gap, we are developing a database catered for historians of education that aims to map out present, past, and future research. Conceived within the framework of Digital Humanities/Digital History, Hecumen is being designed, with the aid of Artificial Intelligence, as an open access finding aid that permits (1) conducting specific and multilevel complex engine searches, (2) having a panoramic view of publications; (3) mapping out relevant/missing areas of research, and, ultimately, (4) keeping up to date with the research produced by historians of education. This paper presents, contextualises, and problematises Hecumen – a digital tool that will facilitate and boost History of Education research. [ABSTRACT FROM AUTHOR]

This paper aims to explore the effect of an artificial intelligence-enabled decision support system (AIDSS) on decision-making processes within a university setting, focusing on the ICT department of a selected South African university. The research objectives revolve around the effectiveness of AIDSS in enhancing decision-making in higher education institutions. Convenience sampling was employed in this study and a questionnaire administered to 28 participants. These methods were chosen to gather insights into the practical application of AI technology in the context of IT operational decision-making. The major findings of this research reveal that AIDSS has the potential to significantly improve decision-making processes in higher education. Despite these advancements, the research acknowledges the ethical and societal implications arising from AI integration, underscoring the importance of achieving a balance between technical advancement and the upholding of human values. As a major policy implication, this study underscores the importance of embracing AI technologies in higher education institutions to enhance decision-making processes, thus improving the efficiency and effectiveness of IT operations. The original contribution of this research lies in its exploration of the practical use of AIDSS in South African higher education, offering insights into how AI can be harnessed to advance decision-making. [ABSTRACT FROM AUTHOR]

In the context of Industry 4.0, integrating Digital Twin (DT) technology stands out as a critical challenge for enhancing manufacturing processes and productivity. The combination of DT and Artificial Intelligence (AI) provides a significant benefit for improving processes in real-time. Industries are actively researching these technologies to keep pace with the rapid evolution of technology, utilizing virtual representations for efficient real-time monitoring and control. The present paper proposes a new approach that relies on DT technology for monitoring, optimizing manufacturing processes and enabling quality control through Deep learning (DL). The proposed methodology involves creating a digital replica of the physical system and utilizing DL models for quality control purposes. This approach improves automation and productivity while maintaining high levels of quality assurance in factories. DL is deployed within the DT for gathering data from the physical system and making predictions regarding product quality. The approach is illustrated by considering an experimental industrial prototype. The results obtained are particularly intriguing, demonstrating heightened predictive accuracy in assessing product quality and real-time issue resolution. Overall, the findings underscore the significant and interesting impact of DT technology with DL on manufacturing processes in the context of Industry 4.0. [ABSTRACT FROM AUTHOR]