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Avoiding financial losses requires preventing catastrophic oil‐filled power transformer breakdowns. Continuous online transformer monitoring is needed. The authors use paper insulation to evaluate transformer health for continuous online transformer monitoring. The study suggests a new artificial intelligence method for estimating paper insulation residual life in oil‐immersed power transformers. The four artificial intelligence models use backpropagation‐based neural networks to predict paper insulation lifespan. Four primary transformer insulating paper failure indices—degree of polymerisation, 2‐furfuraldehyde, carbon monoxide, and carbon dioxide—form the basis of these models. Each model, including the backpropagation‐based neural networks, estimates paper insulation life using one failure index, along with moisture and temperature data. Optimisation techniques enhance hidden layer neurons and epoch count for improved performance. Results are validated against literature‐based life models, establishing a precise input–output correlation. This method accurately predicts the remaining useable life of power transformer paper insulation, enabling utilities to take proactive measures for safe and efficient transformer operation. [ABSTRACT FROM AUTHOR]

Reviews have long been recognized as among the most important forms of scientific communication. The rapid growth of the primary literature has further increased the need for reviews to distill and interpret the literature. This review on Reviews and Reviewing: Approaches to Research Synthesis encompasses the evolution of the review literature, taxonomy of review literature, uses and users of reviews, the process of preparing reviews, assessment of review quality and impact, the impact of information technology on the preparation of reviews, and research opportunities for information science related to reviews and reviewing. In addition to providing a synthesis of prior research, this review seeks to identify gaps in the published research and to suggest possible future research directions. [ABSTRACT FROM AUTHOR]

This article discusses OpenAI's ChatGPT, a generative pre‐trained transformer, which uses natural language processing to fulfill text‐based user requests (i.e., a "chatbot"). The history and principles behind ChatGPT and similar models are discussed. This technology is then discussed in relation to its potential impact on academia and scholarly research and publishing. ChatGPT is seen as a potential model for the automated preparation of essays and other types of scholarly manuscripts. Potential ethical issues that could arise with the emergence of large language models like GPT‐3, the underlying technology behind ChatGPT, and its usage by academics and researchers, are discussed and situated within the context of broader advancements in artificial intelligence, machine learning, and natural language processing for research and scholarly publishing. [ABSTRACT FROM AUTHOR]

Artificial intelligence is more present than ever, both in our society in general and in science. At the center of this development has been the concept of deep learning, the use of artificial neural networks that are many layers deep and can often reproduce human‐like behavior much better than other machine‐learning techniques. The articles in this collection are some recent examples of its application for X‐ray photon science and crystallography that have been published in Journal of Applied Crystallography. [ABSTRACT FROM AUTHOR]

The article provides a preview of forthcoming papers accepted for publication in the Health Information and Libraries Journal. Topics include the effectiveness of citation searching in urgent and emergency care configuration studies, an alternative screening approach for Google Search in systematic reviews, and the analysis of health information seeking behavior among adolescents.

The article discusses the use of chatbot technology in the field of microscopy. The authors, Filip Braet and David Poger, highlight the potential benefits of chatbots in providing access to academic information and datasets. However, they also acknowledge the challenges and ethical considerations associated with using this technology in research. The authors share their experiences working with chatbots and express curiosity about the future role of AI in microscopy. They emphasize the importance of responsible and ethical use of AI in research and education. [Extracted from the article]

This Editorial introduces a Special Issue of papers from the SEE International Conference RADAR 2019, which took place in Toulon, France, 23–27 September 2019. The papers are expanded versions of ones that were judged in the reviewing process to be of the highest quality and relevance. Benefitting from the unique location of the conference centre on the French Riviera, the conference was given the theme 'Sensing from sea to space', attracting nearly 400 attendees from 26 countries from all over the world (Figure 1). Engineers and researchers coming from private or public sector (universities and laboratories) shared their knowledge and experience on radar topics of common interests from physics to signal processing, from sensor validation to system design, and also on emerging technologies based on artificial intelligence; all topics being supported by 200 papers presented either in oral (150 papers in 36 sessions), or poster sessions (50 papers). Figure 2 shows the range of topics of the papers. 1 FIGURE: Conference attendees by country 2 FIGURE: Conference paper topics [ABSTRACT FROM AUTHOR]

An editorial is presented the journal's success with an impact factor of 3.0 and offers readers an extensive collection of publications in critical care nursing research; it discusses the capabilities and limitations of large language models (LLMs) in research, presents various research articles with diverse topics and methodologies, emphasizing the use of LLMs in healthcare research, and highlights the importance of domain-specific expertise when utilizing LLMs.

Artificial intelligence (AI) and natural language processing technologies have fuelled the growth of Pedagogical Conversational Agents (PCAs) with empathic conversational capabilities. However, no systematic literature review has explored the intersection between conversational agents, education and emotion. Therefore, this study aimed to outline the key aspects of designing, implementing and evaluating these agents. The data sources were empirical studies, including peer‐reviewed conference papers and journal articles, and the most recent publications, from the ACM Digital Library, IEEE Xplore, ProQuest, ScienceDirect, Scopus, SpringerLink, Taylor & Francis Online, Web of Science and Wiley Online Library. The remaining papers underwent a rigorous quality assessment. A filter study meeting the objective was based on keywords. Comparative analysis and synthesis of results were used to handle data and combine study outcomes. Out of 1162 search results, 13 studies were selected. The results indicate that agents promote dialogic learning, proficiency in knowledge domains, personalized feedback and empathic abilities as essential design principles. Most implementations employ a quantitative approach, and two variables are used for evaluation. Feedback types play a vital role in achieving positive results in learning performance and student perceptions. The main limitations and gaps are the time range for literature selection, the level of integration of the empathic field and the lack of a detailed development stage report. Moreover, future directions are the ethical implications of agents operating beyond scheduled learning times and the adoption of Responsible AI principles. In conclusion, this review provides a comprehensive framework of empathic PCAs, mostly in their evaluation. The systematic review registration number is osf.io/3xk6a.Practitioner notesWhat is already known about this topicEmotions play a pivotal role in shaping the interaction process, making it essential to consider them when designing methodological strategies or learning tools.Empathic Pedagogical Conversational Agents (PCAs) have emerged as a crucial approach for enhancing and personalizing the learning experience (24/7) for pupils and supporting human teachers in their teaching process.Despite the creation of numerous empathic PCAs, there is a scarcity of Systematic Literature Reviews (SLRs) on their application in the educational field, particularly concerning the integration of emotional abilities in combination with the competencies of each subject.What this paper addsIt offers new insights into the design principles underlying the integration of the empathic field.It reviews different approaches for incorporating students' prior knowledge in real time.It provides a comprehensive and up‐to‐date overview of the research designs used for implementation, including quantitative, qualitative and mixed methods.It examines the factors that influence the effectiveness of empathic PCA in teaching and learning.It evaluates the types of feedback that enhance the impact of the empathic field on learning outcomes.Implications for practice and/or policyIt is crucial to grasp the topics that this paper introduces in order to effectively integrate new learning tools into any context.Techno‐pedagogical designers seeking to gain insights into empathic PCAs will find immense value in this SLR, as it comprehensively covers each stage of the process.For future research endeavours, this study offers a wealth of ideas to draw upon, enabling researchers to address the challenges outlined and explore new avenues of investigation. [ABSTRACT FROM AUTHOR]

This paper combines artificial intelligence deep learning technology to optimize the wine making process of mango wine. Moreover, in view of the shortcomings of traditional electronic nose data processing methods, a deep learning method based on SSAE‐BPNN is proposed for electronic nose data processing. In addition, according to the characteristics of automatic learning features, this paper uses a deep learning method based on SSAE‐BPNN to simplify the process of traditional data processing methods. Finally, this paper constructs an electronic nose system that can be used to identify mango wine making characteristics, and enhances the effect of electronic nose recognition through deep learning. Through the analysis, it can be seen that the mango wine making process optimization method based on artificial intelligence deep learning technology proposed in this paper has a certain effect, and it has optimized the traditional mango wine making process. [ABSTRACT FROM AUTHOR]

This systematic review, adhering to the PRISMA framework, investigated the utilisation of ChatGPT, a language model developed by OpenAI, throughout Kindergarten to 12th grade (K‐12) educational settings. The review synthesises findings from 13 selected papers, encompassing the strengths, weaknesses, opportunities, and threats (SWOT) analysis of ChatGPT's implementation in K‐12 education, implications for various stakeholders, and practical recommendations. It is highlighted that ChatGPT could empower educators through curriculum, lesson planning, materials generation, differentiation, and optimising student learning experience through personalised learning. However, concerns regarding academic integrity and output quality must be addressed. The paper provides pedagogical recommendations and ethical considerations to utilise ChatGPT better. It contributes to the ongoing discourse about AI, particularly ChatGPT's role in K‐12 education, further inspiring future research and educational practices and facilitating the effective integration of ChatGPT into K‐12 educational settings where collaboration arises as a key role, in particular under the approach of co‐design for learning. [ABSTRACT FROM AUTHOR]

In the ever‐evolving AI‐driven education, integrating AI technologies into teaching practices has become increasingly imperative for aspiring STEM educators. Yet, there remains a dearth of studies exploring pre‐service STEM teachers' readiness to incorporate AI into their teaching practices. This study examined the factors influencing teachers' willingness to integrate AI (WIAI), especially from the perspective of pre‐service STEM teachers' attitudes towards the application of AI in teaching. In the study, a comprehensive survey was conducted among 239 pre‐service STEM teachers, examining the influences and interconnectedness of Technological Pedagogical Content Knowledge (TPACK), Perceived Usefulness (PU), Perceived Ease of Use (PE), and Self‐Efficacy (SE) on WIAI. Structural Equation Modeling (SEM) was employed for data analysis. The findings illuminated direct influences of TPACK, PU, PE, and SE on WIAI. TPACK was found to directly affect PE, PU, and SE, while PE and PU also directly influenced SE. Further analysis revealed significant mediating roles of PE, PU, and SE in the relationship between TPACK and WIAI, highlighting the presence of a chain mediation effect. In light of these insights, the study offers several recommendations on promoting pre‐service STEM teachers' willingness to integrate AI into their teaching practices. Practitioner notes What is already known about this topic? The potential of AI technologies to enrich learning experiences and improve outcomes in STEM education has been recognized. Pre‐service teachers' willingness to integrate AI into teaching practice is crucial for shaping the future learning environment. The TAM and TPACK frameworks are used to analyse teacher factors in technology‐supported learning environments. Few studies have been conducted for examining factors of pre‐service teachers' willingness to integrate AI into teaching practices in the context of STEM education. What this paper adds? A survey was designed and developed for exploring pre‐service STEM teachers' WIAI and its relationships with factors including TPACK, PE, PU, and SE. TPACK, SE, PU, and PE have direct impact on pre‐service STEM teachers' WIAI. SE, PU, and PE have been identified as mediating variables in the relationship between TPACK and WIAI. Two sequential mediation effects, TPACK → PE → SE → WIAI and TPACK → PU → SE → WIAI, among pre‐service STEM teachers were further identified. Implications of this study for practice and/or policy Pre‐service STEM teachers are encouraged to explore and utilize AI technology to enhance their confidence and self‐efficacy in integrating AI into teaching practices. Showcasing successful cases and practical experiences is essential for fostering awareness of AI integration in STEM education. It is recommended to introduce AI education courses in teacher training programs. Offering internship and practicum opportunities related to AI technologies can enhance their practical skills in integrating AI into education. What is already known about this topic? The potential of AI technologies to enrich learning experiences and improve outcomes in STEM education has been recognized. Pre‐service teachers' willingness to integrate AI into teaching practice is crucial for shaping the future learning environment. The TAM and TPACK frameworks are used to analyse teacher factors in technology‐supported learning environments. Few studies have been conducted for examining factors of pre‐service teachers' willingness to integrate AI into teaching practices in the context of STEM education. What this paper adds? A survey was designed and developed for exploring pre‐service STEM teachers' WIAI and its relationships with factors including TPACK, PE, PU, and SE. TPACK, SE, PU, and PE have direct impact on pre‐service STEM teachers' WIAI. SE, PU, and PE have been identified as mediating variables in the relationship between TPACK and WIAI. Two sequential mediation effects, TPACK → PE → SE → WIAI and TPACK → PU → SE → WIAI, among pre‐service STEM teachers were further identified. Implications of this study for practice and/or policy Pre‐service STEM teachers are encouraged to explore and utilize AI technology to enhance their confidence and self‐efficacy in integrating AI into teaching practices. Showcasing successful cases and practical experiences is essential for fostering awareness of AI integration in STEM education. It is recommended to introduce AI education courses in teacher training programs. Offering internship and practicum opportunities related to AI technologies can enhance their practical skills in integrating AI into education. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) technology has brought new reconstruction opportunities for the intelligence of the advertisement industry through the help of AI technologies such as machine learning and deep learning. First, the relationship between AI and the attractiveness of green advertisements is investigated, and the influence of different AI technologies in green advertisements on consumers' perception of the attractiveness of green advertisements is summarized. Second, based on the green advertisement dissemination rate data set, the data visualization exploration is carried out, and the data deletion and coding processing are carried out aiming at different characteristic variables. Finally, according to the problems existing in the current green advertisement communication and the high‐dimensional and sparse characteristics of the communication rate data set. In this paper, based on Deep FM (Factorization Machine), Gradient Boost Decision Tree (GBDT) is added to assist the experiment, and the prediction performance of green advertising communication is tested. The results are as follows. (1) Different AI expressions in green advertisements will affect consumers' perception of the attractiveness of green advertisements. (2) The prediction ability of Deep FM model after feature engineering is better than that of data cleaning only. The prediction effect of the model is obviously improved. The purpose of this paper is to integrate green advertising media communication into the ecological concept of harmonious coexistence between man and nature, strengthen the political belief of ecological civilization construction, and conform to the communication trend of today's severe ecological situation. [ABSTRACT FROM AUTHOR]

Artificial intelligence technology is constantly developing and changing. Due to the continuous development of technology and production of carbon emissions, various problems have occurred, forcing various regions to start promoting low‐carbon work. What kind of method can effectively and quickly improve the performance of carbon emissions has become one of the current research topics that has attracted much attention. In response to this problem, it is of great significance to study in the field of carbon emission performance improvement methods. With the in‐depth research on carbon emissions, the research on the intervention of water conservancy policies in carbon emissions has gradually been carried out, and its functional effects are of great significance to solve the problem of improving carbon emissions performance. This paper has aimed to study the impact and mechanism of water conservancy policy on carbon emission performance in the context of artificial intelligence. Through the analysis and research of the water conservancy policy under artificial intelligence, the double difference model (DID) is used to evaluate it, so that it can show the impact and mechanism on carbon emission performance, so as to solve the problem of improving carbon emission performance. This paper has analysed water policy, carbon emission performance and DID model under artificial intelligence. Its influence and mechanism have been experimentally analysed, and related theoretical formulas have been used to explain. The results have shown that the water conservancy policy has a significant and continuous driving effect on the reduction of carbon emission intensity, and its interaction coefficient under the control variable is −0.934, which is significantly negative. Further analysis of variables has shown that the energy intensity coefficient is 0.714, and the secondary industry coefficient is 0.924, which are both positive and significant. Water conservancy policies mainly reduce carbon intensity through the improvement of energy efficiency and industrial structure upgrading. It can be seen that water conservancy policies under artificial intelligence can meet the needs of improving carbon emission performance, and the level of economic growth and green development has been greatly improved. [ABSTRACT FROM AUTHOR]

Adsorption cooling and desalination systems have a distinct advantage over other systems that use low‐grade waste heat near ambient temperature. Since improving their performance, including reliability and failure prediction, is challenging, developing an efficient diagnostic system is of great practical significance. The paper introduces artificial intelligence (AI) and an automated machine learning approach (AutoML) in a real‐life application for a computational diagnostic system of existing adsorption cooling and desalination facilities. A total of 1769 simulated data points containing data indicating a failure status are applied to develop a comprehensive AI‐based Diagnostic (AID) system covering a wide range of 42 input parameters. The paper introduces a conditional monitoring system for adsorption cooling and desalination systems. The novelty of the presented study mainly consists of two aspects. First, the intelligent system predicts the health or failure states of various components in a complex three‐bed adsorption chiller installation using the extensive input data sets of 42 different operating parameters. The developed AID expert tool, based on selecting the best from 42 models generated by the DataRobot platform, was validated on the complex, existing three‐bed adsorption chiller. The AID system correctly identified healthy and failure states in various installation components. The developed expert system is very efficient (AUC = 0.988, RMSE = 0.20, LogLoss = 0.14) in predicting emergency states. The proposed method constitutes a quick and easy technique for failure prediction and represents a complementary tool compared to the other condition monitoring methods. [ABSTRACT FROM AUTHOR]

Background: Driven by the ongoing need to provide high‐quality learning and teaching, universities recently have shown an increased interest in using learning analytics (LA) for improving learning design (LD). However, the evidence of such improvements is scarce, and the maturity of such research is unclear. Objectives: This study is aimed to evaluate the maturity of research discussing LA‐driven LD improvements in higher education. Methods: The systematic review analyses 49 empirical papers, assesses their quality and suggests further research directions. The review elaborates on methodological (research questions, strategy and methods, LA‐LD integration theoretical backgrounds) and substantial (LA‐driven LD improvements, types of data used, LA software development) features of the papers. Results and Conclusions: The findings demonstrated the lack of theoretical alignment between LA and LD, with research tending towards user experience studies. The most frequently used research strategy was a case study; experiments were very rare. Researchers predominantly used parsing for collecting data and AI methods for analysing it; mostly used data types related to registering learners' engagement with learning activities as well as resources and tools provided in digital learning environments. Takeaways: The research area discussing LA‐driven LD improvements still has a way to go before attaining the level of full maturity. Only a third of the papers reported actual LA‐driven LD improvements; moreover, only three papers measured their effectiveness. The presented LA software was mostly at the beta or implementation stages and did not assess the impact of using this software. Lay Description: What is already known about this topic: Universities show an increased interest in using LA for improving LD.The evidence of such improvements being efficient is scarce.Learning analytics is weakly grounded in learning and teaching. What this paper adds: LA‐LD research presented in the reviewed papers cannot yet be considered fully mature.Almost half of the research was carried out using a case study approach, intrinsically challenging in terms of replication and validity. As few as three out of the reviewed studies applied an experimental approach, capable of stating the cause–effect relationship between LA and LD.All the studied papers mentioned improvements in LD, but only around a third of them reported actual improvements, another one‐third suggested potential improvements, and the rest did not come down to describing any.The LA software presented in the papers was mostly at the testing or implementation stages, and the impact of implemented software on LD has yet to be evaluated. Implications for practice and/or policy: Future LA‐LD research to become more mature may well elaborate on theoretical footing, contribute to the cause–effect relationship between LA and LD utilising experimental research design, ensure sufficient sample size.Practitioners' efforts should be directed to operationalising LD for efficient measurements, addressing pedagogical challenges when applying LA, and assessing LA‐driven improvements introduced into LD. [ABSTRACT FROM AUTHOR]

The purpose of this study was to discuss how artificial intelligence (AI) methods have affected the field of drug development. It looks at how AI models and data resources are reshaping the drug development process by offering more affordable and expedient options to conventional approaches. The paper opens with an overview of well‐known information sources for drug development. The discussion then moves on to molecular representation techniques that make it possible to convert data into representations that computers can understand. The paper also gives a general overview of the algorithms used in the creation of drug discovery models based on AI. In particular, the paper looks at how AI algorithms might be used to forecast drug toxicity, drug bioactivity, and drug physicochemical properties. De novo drug design, binding affinity prediction, and other AI‐based models for drug–target interaction were covered in deeper detail. Modern applications of AI in nanomedicine design and pharmacological synergism/antagonism prediction were also covered. The potential advantages of AI in drug development are highlighted as the evaluation comes to a close. It underlines how AI may greatly speed up and improve the efficiency of drug discovery, resulting in the creation of new and better medicines. To fully realize the promise of AI in drug discovery, the review acknowledges the difficulties that come with its uses in this field and advocates for more study and development. [ABSTRACT FROM AUTHOR]

The amount of grey literature and 'softer' intelligence from social media or websites is vast. Given the long lead‐times of producing high‐quality peer‐reviewed health information, this is causing a demand for new ways to provide prompt input for secondary research. To our knowledge, this is the first review of automated data extraction methods or tools for health‐related grey literature and soft data, with a focus on (semi)automating horizon scans, health technology assessments (HTA), evidence maps, or other literature reviews. We searched six databases to cover both health‐ and computer‐science literature. After deduplication, 10% of the search results were screened by two reviewers, the remainder was single‐screened up to an estimated 95% sensitivity; screening was stopped early after screening an additional 1000 results with no new includes. All full texts were retrieved, screened, and extracted by a single reviewer and 10% were checked in duplicate. We included 84 papers covering automation for health‐related social media, internet fora, news, patents, government agencies and charities, or trial registers. From each paper, we extracted data about important functionalities for users of the tool or method; information about the level of support and reliability; and about practical challenges and research gaps. Poor availability of code, data, and usable tools leads to low transparency regarding performance and duplication of work. Financial implications, scalability, integration into downstream workflows, and meaningful evaluations should be carefully planned before starting to develop a tool, given the vast amounts of data and opportunities those tools offer to expedite research. [ABSTRACT FROM AUTHOR]

Background: The emergence of the artificial intelligence chatbot ChatGPT in November 2022 has garnered substantial attention across diverse disciplines. Despite widespread adoption in various sectors, the exploration of its application in libraries, especially within the medical domain, remains limited. Aims/Objectives: Many areas of interest remain unexplored like ChatGPT in medical libraries and this review aims to synthesise what is currently known about it to identify gaps and stimulate further research. Methods: Employing Cooper's integrative review method, this study involves a comprehensive analysis of existing literature on ChatGPT and its potential implementations within library contexts. Results: A systematic literature search across various databases yielded 166 papers, with 30 excluded for irrelevance. After abstract reviews and methodological assessments, 136 articles were selected. Critical Appraisal Skills Programme qualitative checklist further narrowed down to 29 papers, forming the basis for the present study. The literature analysis reveals diverse applications of ChatGPT in medical libraries, including aiding users in finding relevant medical information, answering queries, providing recommendations and facilitating access to resources. Potential challenges and ethical considerations associated with ChatGPT in this context are also highlighted. Conclusion: Positioned as a review, our study elucidates the applications of ChatGPT in medical libraries and discusses relevant considerations. The integration of ChatGPT into medical library services holds promise for enhancing information retrieval and user experience, benefiting library users and the broader medical community. [ABSTRACT FROM AUTHOR]

This study investigated the added value of real‐time adaptive feedback on seventh graders' performances in tablet‐based geometry learning. To isolate the effects of the medium (ie, tablet) from those of the feedback, three groups were compared: paper‐and‐pencil, pen‐based tablet without feedback and pen‐based tablet with feedback. The feedback was provided by a tutoring system based on an artificial intelligence that automatically interpreted students' pen strokes on the screen. A total of 85 French students drew three geometric shapes, either on paper or on a tablet, and then performed a transfer task on paper. Results showed that using a tablet without feedback did not improve learning but seemed to enhance interest in the task compared to the paper‐and‐pencil group. Students in the tablet with feedback group performed significantly better than the other two groups on learning, as well as on transfer. This study was the first to combine media comparison and added‐value approaches to test the effects on students' geometry performances of using a new educational app on a pen‐based tablet in a naturalistic classroom environment. Results showed that it was not the medium used but the intelligent tutoring system‐based feedback that improved students' performance. Our data therefore indicate that artificial intelligence is a promising way of providing learners with real‐time adaptive feedback in order to improve their performances. Practitioner notes What is already known about this topic Previous meta‐analyses have investigated the effects of tablet‐based learning. Tablet computers have been proven to increase students' motivation. Yet, the influence of tablet computers on learning outcomes remains inconclusive. Other studies show that certain features of environments, such as feedback, have positive effects on learning. What this paper adds Most of the previous studies adopted a media comparison approach (paper‐ vs. tablet‐based instruction). We combine this approach with an added‐value approach by adding or not real‐time AI‐based feedback. Results showed that tablet use increased children's interest but not their learning outcomes. Feedback improved children's performance in a training task and a later transfer paper task. Implications for practice and/or policy Tablet computers can promote students' interest in the task during geometry instruction. App features play a critical role in improving students' learning. Specifically, IA‐based adaptive feedback helps children to perform better on a geometry task. What is already known about this topic Previous meta‐analyses have investigated the effects of tablet‐based learning. Tablet computers have been proven to increase students' motivation. Yet, the influence of tablet computers on learning outcomes remains inconclusive. Other studies show that certain features of environments, such as feedback, have positive effects on learning. What this paper adds Most of the previous studies adopted a media comparison approach (paper‐ vs. tablet‐based instruction). We combine this approach with an added‐value approach by adding or not real‐time AI‐based feedback. Results showed that tablet use increased children's interest but not their learning outcomes. Feedback improved children's performance in a training task and a later transfer paper task. Implications for practice and/or policy Tablet computers can promote students' interest in the task during geometry instruction. App features play a critical role in improving students' learning. Specifically, IA‐based adaptive feedback helps children to perform better on a geometry task. [ABSTRACT FROM AUTHOR]

This document is a guest editorial from the journal IET Radar, Sonar & Navigation. It discusses the advances in AI-assisted radar sensing applications and the challenges that hinder its adoption in this field. The special issue of the journal features nine papers that address these challenges and offer innovative ideas and experimental results. The papers cover a range of topics, including health monitoring, human activity recognition, voice identification, elderly care health monitoring, track-to-track association, signal pre-processing, traffic congestion alleviation, and target recognition. The authors express their gratitude to the contributors and reviewers and believe that the research presented will inspire further exploration and innovation in this field. [Extracted from the article]

Technology is being developed to support the peer review processes of journals, conferences, funders, universities, and national research evaluations. This literature and software summary discusses the partial or complete automation of several publishing‐related tasks: suggesting appropriate journals for an article, providing quality control for submitted papers, finding suitable reviewers for submitted papers or grant proposals, reviewing, and review evaluation. It also discusses attempts to estimate article quality from peer review text and scores as well as from post‐publication scores but not from bibliometric data. The literature and existing examples of working technology show that automation is useful for helping to find reviewers and there is good evidence that it can sometimes help with initial quality control of submitted manuscripts. Much other software supporting publishing and editorial work exists and is being used, but without published academic evaluations of its efficacy. The value of artificial intelligence (AI) to support reviewing has not been clearly demonstrated yet, however. Finally, whilst peer review text and scores can theoretically have value for post‐publication research assessment, it is not yet widely enough available to be a practical evidence source for systematic automation. [ABSTRACT FROM AUTHOR]

The article provides an overview of the journal Molecular Ecology (MEC) and its achievements in the fields of ecology and evolution. It discusses the journal's high ranking and impact factor, as well as its policies on data and code archiving, indigenous perspectives, and the use of artificial intelligence. The article also mentions the journal's social media presence and blog. It concludes with information about prizes and notable papers published in the journal. The text discusses various research articles, opinion papers, and special issues covering a range of topics in molecular ecology. The journal aims to provide valuable insights and promote discussion in the field. The given text is an editorial written by several authors, but no information about its content is provided. [Extracted from the article]

Artificially intelligent systems (AISs) are being created by software developing companies (SDCs) to influence clinical decision‐making. Historically, clinicians have led healthcare decision‐making, and the introduction of AISs makes SDCs novel actors in the clinical decision‐making space. Although these AISs are intended to influence a clinician's decision‐making, SDCs have been clear that clinicians are in fact the final decision‐makers in clinical care, and that AISs can only inform their decisions. As such, the default position is that clinicians should hold responsibility for the outcomes of the use of AISs. This is not the case when an AIS has influenced a clinician's judgement and their subsequent decision. In this paper, we argue that this is an imbalanced and unjust position, and that careful thought needs to go into how personal moral responsibility for the use of AISs in clinical decision‐making should be attributed. This paper employs and examines the difference between prospective and retrospective responsibility and considers foreseeability as key in determining how personal moral responsibility can be justly attributed. This leads us to the view that moral responsibility for the outcomes of using AISs in healthcare ought to be shared by the clinical users and SDCs. [ABSTRACT FROM AUTHOR]

Educational technology innovations leveraging large language models (LLMs) have shown the potential to automate the laborious process of generating and analysing textual content. While various innovations have been developed to automate a range of educational tasks (eg, question generation, feedback provision, and essay grading), there are concerns regarding the practicality and ethicality of these innovations. Such concerns may hinder future research and the adoption of LLMs‐based innovations in authentic educational contexts. To address this, we conducted a systematic scoping review of 118 peer‐reviewed papers published since 2017 to pinpoint the current state of research on using LLMs to automate and support educational tasks. The findings revealed 53 use cases for LLMs in automating education tasks, categorised into nine main categories: profiling/labelling, detection, grading, teaching support, prediction, knowledge representation, feedback, content generation, and recommendation. Additionally, we also identified several practical and ethical challenges, including low technological readiness, lack of replicability and transparency and insufficient privacy and beneficence considerations. The findings were summarised into three recommendations for future studies, including updating existing innovations with state‐of‐the‐art models (eg, GPT‐3/4), embracing the initiative of open‐sourcing models/systems, and adopting a human‐centred approach throughout the developmental process. As the intersection of AI and education is continuously evolving, the findings of this study can serve as an essential reference point for researchers, allowing them to leverage the strengths, learn from the limitations, and uncover potential research opportunities enabled by ChatGPT and other generative AI models. Practitioner notesWhat is currently known about this topic Generating and analysing text‐based content are time‐consuming and laborious tasks.Large language models are capable of efficiently analysing an unprecedented amount of textual content and completing complex natural language processing and generation tasks.Large language models have been increasingly used to develop educational technologies that aim to automate the generation and analysis of textual content, such as automated question generation and essay scoring.What this paper adds A comprehensive list of different educational tasks that could potentially benefit from LLMs‐based innovations through automation.A structured assessment of the practicality and ethicality of existing LLMs‐based innovations from seven important aspects using established frameworks.Three recommendations that could potentially support future studies to develop LLMs‐based innovations that are practical and ethical to implement in authentic educational contexts.Implications for practice and/or policy Updating existing innovations with state‐of‐the‐art models may further reduce the amount of manual effort required for adapting existing models to different educational tasks.The reporting standards of empirical research that aims to develop educational technologies using large language models need to be improved.Adopting a human‐centred approach throughout the developmental process could contribute to resolving the practical and ethical challenges of large language models in education. [ABSTRACT FROM AUTHOR]

Parkinson's Disease (PD) is a brain‐related disease that eventually causes disability and disrupts a person's normal life. Most physicians and researchers try to diagnose it quickly and treat it on time. In recent years, computer science and the field of artificial intelligence (machine learning) have helped researchers find a way to detect the disease early. This article proposes a method that diagnoses Parkinson's disease by analyzing the hand drawing shaped by the individuals using Bi‐Directional Long Short‐Term Memory (Bi‐LSTM) neural network. In addition, this paper proposes a Fuzzy Inferential Classifier for the Dense layer, which classifies the output of LSTM Blocks to the associated classes by modifying the Soft‐max function. Our decision to propose this classifier was because, in some cases, hand‐drawing data related to people with Parkinson's disease have no significant difference with the healthy subjects for the distinguishing and are often very similar to each other. A standard dataset has been used in this paper, which includes spirals drawn test (including static spiral, dynamic spiral, and stability specific point) by a group of healthy people and people with Parkinson's disease. The proposed method has reached 97%, 98.5%, and 100% accuracy rates for the three mentioned spiral tests with a smoother training loss and accuracy plots. In addition, the results outperform state‐of‐the‐art research conducted on this dataset and show at least more than 2.5% improvement in the accuracy rate in comparison. [ABSTRACT FROM AUTHOR]

Aim Methods Results Conclusions ChatGPT's role in medical writing is a topic of discussion. I experimented whether ChatGPT almost automatically generates Correspondence or Letter addressed to a “translated” article, and thereby wish to arouse discussion regarding ChatGPT use in medical writing.I input an English article of mine into ChatGPT, tasking it with generating an English Disagreement Letter (Letter 1). Next, I tasked ChatGPT with translating the manuscript addressed to from English‐French‐Spanish‐German. Then, I once again tasked ChatGPT with generating an English Disagreement Letter addressed to a German manuscript (triplicate translated manuscript) (Letter 2).Letters 1 and 2 are readable and reasonable, shooting the point that the author (myself) felt as the weakness of the article. Letters addressed to French (single translation) and to Spanish (double translation) and longer Letters (corresponding to Letters 1 and 2) are also readable, and thus stand.Solely based on this experiment, one may be able to write a letter even without understanding the meaning of the paper being addressed, let alone the language of the paper. Although this humble experiment does not conclude anything, I plea for a comprehensive discussion on the implications of these findings. [ABSTRACT FROM AUTHOR]