1,772 research outputs found

    Reimagining the Journal Editorial Process: An AI-Augmented Versus an AI-Driven Future

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    The editorial process at our leading information systems journals has been pivotal in shaping and growing our field. But this process has grown long in the tooth and is increasingly frustrating and challenging its various stakeholders: editors, reviewers, and authors. The sudden and explosive spread of AI tools, including advances in language models, make them a tempting fit in our efforts to ease and advance the editorial process. But we must carefully consider how the goals and methods of AI tools fit with the core purpose of the editorial process. We present a thought experiment exploring the implications of two distinct futures for the information systems powering today’s journal editorial process: an AI-augmented and an AI-driven one. The AI-augmented scenario envisions systems providing algorithmic predictions and recommendations to enhance human decision-making, offering enhanced efficiency while maintaining human judgment and accountability. However, it also requires debate over algorithm transparency, appropriate machine learning methods, and data privacy and security. The AI-driven scenario, meanwhile, imagines a fully autonomous and iterative AI. While potentially even more efficient, this future risks failing to align with academic values and norms, perpetuating data biases, and neglecting the important social bonds and community practices embedded in and strengthened by the human-led editorial process. We consider and contrast the two scenarios in terms of their usefulness and dangers to authors, reviewers, editors, and publishers. We conclude by cautioning against the lure of an AI-driven, metric-focused approach, advocating instead for a future where AI serves as a tool to augment human capacity and strengthen the quality of academic discourse. But more broadly, this thought experiment allows us to distill what the editorial process is about: the building of a premier research community instead of chasing metrics and efficiency. It is up to us to guard these values

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    An xAPI application profile to monitor self-regulated learning strategies

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    Self-regulated learning (SRL) is being promoted and adopted increasingly due to the needs of current education, student centered and focused on competence development. One of the main components of SRL is learners' self-monitoring, which eventually contributes to a better performance. Monitoring is also important for teachers, as it enables them to know to what extent their learners are doing well and progressing properly. At the same time, the use of technology for learning is now common and facilitates monitoring. Nevertheless, the available software still offers poor support from the SRL point of view, especially, for SRL monitoring. This clashes with the growth of learning analytics and educational data mining. The main issue is the wide variety of SRL actions that need to be captured, commonly performed in different tools, and the need to integrate them to support the development of analytics and data mining developments, making imperative the search of interoperable solutions. This paper focuses on the standardization of SRL traces to enable data collection from multiple sources and data analysis with the goal of easing the monitoring process for teachers and learners. First, the paper analyzes current monitoring software and its limitations for SRL. Then, after a brief analysis of available standards on this area, an application profile for the eXperience API specification is proposed to enable the interoperable recording of the SRL traces. The paper describes the process followed to create the profile, from the analysis to the final implementation, including the selection of the interactions that represent relevant SRL actions, the selection of vocabularies to record them and a case study.Xunta de Galicia | Ref. ED431B 2017/67Xunta de Galicia | Ref. ED431D 2017/1

    AI: Limits and Prospects of Artificial Intelligence

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    The emergence of artificial intelligence has triggered enthusiasm and promise of boundless opportunities as much as uncertainty about its limits. The contributions to this volume explore the limits of AI, describe the necessary conditions for its functionality, reveal its attendant technical and social problems, and present some existing and potential solutions. At the same time, the contributors highlight the societal and attending economic hopes and fears, utopias and dystopias that are associated with the current and future development of artificial intelligence

    30th European Congress on Obesity (ECO 2023)

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    This is the abstract book of 30th European Congress on Obesity (ECO 2023

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Hybrid human-AI driven open personalized education

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    Attaining those skills that match labor market demand is getting increasingly complicated as prerequisite knowledge, skills, and abilities are evolving dynamically through an uncontrollable and seemingly unpredictable process. Furthermore, people's interests in gaining knowledge pertaining to their personal life (e.g., hobbies and life-hacks) are also increasing dramatically in recent decades. In this situation, anticipating and addressing the learning needs are fundamental challenges to twenty-first century education. The need for such technologies has escalated due to the COVID-19 pandemic, where online education became a key player in all types of training programs. The burgeoning availability of data, not only on the demand side but also on the supply side (in the form of open/free educational resources) coupled with smart technologies, may provide a fertile ground for addressing this challenge. Therefore, this thesis aims to contribute to the literature about the utilization of (open and free-online) educational resources toward goal-driven personalized informal learning, by developing a novel Human-AI based system, called eDoer. In this thesis, we discuss all the new knowledge that was created in order to complete the system development, which includes 1) prototype development and qualitative user validation, 2) decomposing the preliminary requirements into meaningful components, 3) implementation and validation of each component, and 4) a final requirement analysis followed by combining the implemented components in order develop and validate the planned system (eDoer). All in all, our proposed system 1) derives the skill requirements for a wide range of occupations (as skills and jobs are typical goals in informal learning) through an analysis of online job vacancy announcements, 2) decomposes skills into learning topics, 3) collects a variety of open/free online educational resources that address those topics, 4) checks the quality of those resources and topic relevance using our developed intelligent prediction models, 5) helps learners to set their learning goals, 6) recommends personalized learning pathways and learning content based on individual learning goals, and 7) provides assessment services for learners to monitor their progress towards their desired learning objectives. Accordingly, we created a learning dashboard focusing on three Data Science related jobs and conducted an initial validation of eDoer through a randomized experiment. Controlling for the effects of prior knowledge as assessed by the pretest, the randomized experiment provided tentative support for the hypothesis that learners who engaged with personal eDoer recommendations attain higher scores on the posttest than those who did not. The hypothesis that learners who received personalized content in terms of format, length, level of detail, and content type, would achieve higher scores than those receiving non-personalized content was not supported as a statistically significant result

    Learning and creating together:Bridging the gap between science and practice through education to enhance person-centered nursing home care

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    Er wordt veel onderzoek gedaan naar de verpleeghuiszorg, die complexer wordt. Ouderen verhuizen naar het verpleeghuis op een steeds hogere leeftijd en zij hebben vaak meerdere zorgvragen. Het is belangrijk dat deze nieuwe wetenschappelijke kennis toekomstige zorgmedewerkers bereikt. Er is echter nog weinig bekend hoe die kennis terechtkomt bij studenten die werken of stagelopen in het verpleeghuis. In dit promotietraject is onderzocht hoe wetenschappelijke kennis bij hen terecht kan komen via onderwijs. Daarnaast is gekeken of deze nieuwe kennis bijdraagt aan de ontwikkeling van mensgerichte verpleeghuiszorg, waarbij écht rekening gehouden wordt met wat de bewoners kunnen, willen en nodig hebben.Belangrijkste bevindingen• Samen ontwikkelen werkt: het proefschrift ‘Learning and creating together’ laat zien dat door samen te ontwikkelen met zorgpraktijk en zorgonderwijs de kloof tussen wetenschap en verpleeghuiszorg verkleind kan worden. Het samen ontwikkelen leverde twee lesmodules op voor zorgstudenten, ontwikkeld met kennis vanuit twee wetenschappelijke onderzoeken. De module ‘Vertel eens! Leren van verhalen’ leert studenten hoe ze de kwaliteit van zorg kunnen verbeteren met verhalen van bewoners. De andere lesmodule, ‘Interprofessioneel leren en evalueren’, bevordert samenwerking tussen verschillende zorgdisciplines om bewoners de juiste zorg en ondersteuning te bieden. Beide lesmodules droegen bij aan de ontwikkeling van de meeste studenten, zowel op de inhoud als op hun mensgerichte werken. • Zelfscan: Samen met experts uit de verpleeghuiszorg en het zorgonderwijs is de Zelfscan Mensgerichte Verpleeghuiszorg ontwikkeld. Hiermee kunnen zorgprofessionals hun mensgerichte aanpak monitoren en verbeteren.• Werkplekleren: Dit promotieonderzoek toont aan dat leren op de werkplek via zorgopleidingen een veelbelovende aanpak is om wetenschap en praktijk te verbinden. Werkplekleren is vaak informeel leren en leren met elkaar. Het is echter nog niet gebruikelijk voor docenten en opleiders om deze vormen van leren te begeleiden en bevorderen op de werkplek. • Onderbelichte mbo-geschoolde zorgprofessionals: Ook toont dit proefschrift aan dat er weinig onderzoek is gedaan naar het leerproces van mbo-geschoolde zorgprofessionals in de verpleeghuiszorg, hoewel zij de grootste beroepsgroep vormen in deze sector. Begrijpen hoe zij leren is van belang voor effectieve kennisdeling. Het proefschrift presenteert een model van beïnvloedende factoren in hun leerproces, dat opleiders en mbo-geschoolde zorgprofessionals ondersteunt bij het maken van hun leerplannen.Belangrijkste aanbevelingen• Effectiviteit van lesmodules: Onderzoek of de lesmodules die in samenwerking zijn ontworpen ook de ontwikkeling van zorgprofessionals bevorderen. • Werkplekgericht zorgonderwijs: Richt het zorgonderwijs meer op de werkplek. Heb hierbij aandacht voor de veranderende rollen van docenten en begeleidende zorgprofessionals. Dit vereist goede afstemming tussen mbo, hbo, wo en de verpleeghuiszorg. De producten die voortkomen uit dit onderzoek zijn hier te downloaden: https://mensgerichteouderenzorg.nl/aan-de-slag/toolboxen-mensgerichte-ouderenzorg

    Measuring the impact of COVID-19 on hospital care pathways

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    Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospital’s new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted

    Industry 4.0 for SME

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    Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsIndustry 4.0 has been growing within companies and impacting the economy and society, but this has been a more complex challenge for some types of companies. Due to the costs and complexity associated with Industry 4.0 technologies, small and medium enterprises face difficulties in adopting them. This thesis proposes to create a model that gives guidance and simplifies how to implement Industry 4.0 in SMEs with a low-cost perspective. It is intended that this model can be used as a blueprint to design and implement an Industry 4.0 project within a manufactory SME. To create the model, a literature review of the different fields regarding Industry 4.0 were conducted to understand the most suited technologies to leverage within the manufacturing industry and the different use cases where these would be applicable. After the model was built, expert interviews were conducted, and based on the received feedback, the model was tweaked, improved, and validated
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