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Smart education systems supported by ICT and AI
Boris Aberšek, Andrej Flogie, 2023, preface, editorial, afterword

Keywords: teaching and learning, smart education systems, education and technology, ICT, artificial intelligence
Published in DKUM: 05.04.2024; Views: 123; Downloads: 0
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The IHI Rochester Report 2022 on healthcare informatics research : Resuming after the CoViD-19
Carlo Combi, Julio C. Facelli, Peter Haddawy, John H. Holmes, Sabine Koch, Hongfang Liu, Jochen Meyer, Mor Peleg, Giuseppe Pozzi, Gregor Štiglic, Pierangelo Veltri, Christopher C. Yang, 2023, review article

Abstract: In 2020, the CoViD-19 pandemic spread worldwide in an unexpected way and suddenly modified many life issues, including social habits, social relationships, teaching modalities, and more. Such changes were also observable in many different healthcare and medical contexts. Moreover, the CoViD-19 pandemic acted as a stress test for many research endeavors, and revealed some limitations, especially in contexts where research results had an immediate impact on the social and healthcare habits of millions of people. As a result, the research community is called to perform a deep analysis of the steps already taken, and to re-think steps for the near and far future to capitalize on the lessons learned due to the pandemic. In this direction, on June 09th–11th, 2022, a group of twelve healthcare informatics researchers met in Rochester, MN, USA. This meeting was initiated by the Institute for Healthcare Informatics—IHI, and hosted by the Mayo Clinic. The goal of the meeting was to discuss and propose a research agenda for biomedical and health informatics for the next decade, in light of the changes and the lessons learned from the CoViD-19 pandemic. This article reports the main topics discussed and the conclusions reached. The intended readers of this paper, besides the biomedical and health informatics research community, are all those stakeholders in academia, industry, and government, who could benefit from the new research findings in biomedical and health informatics research. Indeed, research directions and social and policy implications are the main focus of the research agenda we propose, according to three levels: the care of individuals, the healthcare system view, and the population view.
Keywords: biomedical and health informatics, artificial intelligence in medicine, research trends, CoViD-19
Published in DKUM: 03.04.2024; Views: 50; Downloads: 3
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Artificial intelligence and business studies : study cycle differences regarding the perceptions of the key future competences
Polona Tominc, Maja Rožman, 2023, original scientific article

Abstract: The purpose of this article is to identify the differences in various aspects of the perception of artificial intelligence by students of economics and business studies at different levels of study and, on this basis, to formulate recommendations both to the higher education institutions themselves, which educate in the field of economic and business sciences, as well as to curriculum designers. First, we utilized descriptive statistics to analyze the responses for each construct among undergraduate and postgraduate students. In the second part, we employed the Kolmogorov-Smirnov and Shapiro-Wilk tests to assess the normality of data distribution. Finally, in the third part, we employed the non-parametric Mann-Whitney U test to identify the differences between undergraduate and postgraduate students. The results show that statistically significant differences can be identified especially in how students of both study levels see and understand the importance of AI. Although we did not identify significant differences between students of both levels in how they see their role in the future labor market, which will be (or already is) characterized by artificial intelligence, we must emphasize that students of both levels evaluate their roles modestly in this respect. Therefore, on this basis, we have made recommendations for more active development and integration of AI in the study process; the article presents important suggestions for improving education to prepare students for the business world of artificial intelligence.
Keywords: artificial intelligence, undergraduate students, postgraduate students, education
Published in DKUM: 03.04.2024; Views: 49; Downloads: 4
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Artificial intelligence and agility-based model for successful project implementation and company competitiveness
Polona Tominc, Dijana Oreški, Maja Rožman, 2023, original scientific article

Abstract: The purpose of the paper is to present a model of factors affecting the successful project implementation by introducing agility and artificial intelligence to increase the company’s competitiveness. In the model, the multidimensional constructs describing the implementation of an agile work environment and artificial intelligence technologies and tools were developed. These multidimensional constructs are agile work environment, agile leadership, agile team skills and capabilities, improving the work of the leader in the project, adopting AI technologies in the project, and using AI solutions in a project. Their impact on successful project implementation and on the company competitiveness was tested. The fundamental reason for conducting this research and developing the model is to enhance the understanding of factors that contribute to the successful implementation of projects and to increase a company’s competitiveness. Our developed model encompasses multidimensional constructs that describe the agile work environment and the utilization of AI technologies. By examining the impact of these constructs on both successful project implementation and company competitiveness, we aimed to establish a comprehensive framework that captures the relationship between agility, AI, and successful project implementation. This model serves as a valuable tool for companies seeking to improve their project implementation processes and gain a competitive edge in the market. The research was based on a sample of 473 managers/owners in medium-sized and large companies. Structural equation modeling was used to test the hypotheses. In today’s turbulent environment, the results will help develop guidelines for a successful combination of agile business practices and artificial intelligence to achieve successful project implementation, increasing a company’s competitiveness.
Keywords: artificial intelligence, agile work environment, company competitiveness, project management
Published in DKUM: 26.03.2024; Views: 51; Downloads: 3
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Agile Machine Learning Model Development Using Data Canyons in Medicine : A Step towards Explainable Artificial Intelligence and Flexible Expert-Based Model Improvement
Bojan Žlahtič, Jernej Završnik, Helena Blažun Vošner, Peter Kokol, David Šuran, Tadej Završnik, 2023, original scientific article

Abstract: Over the past few decades, machine learning has emerged as a valuable tool in the field of medicine, driven by the accumulation of vast amounts of medical data and the imperative to harness this data for the betterment of humanity. However, many of the prevailing machine learning algorithms in use today are characterized as black-box models, lacking transparency in their decision-making processes and are often devoid of clear visualization capabilities. The transparency of these machine learning models impedes medical experts from effectively leveraging them due to the high-stakes nature of their decisions. Consequently, the need for explainable artificial intelligence (XAI) that aims to address the demand for transparency in the decision-making mechanisms of black-box algorithms has arisen. Alternatively, employing white-box algorithms can empower medical experts by allowing them to contribute their knowledge to the decision-making process and obtain a clear and transparent output. This approach offers an opportunity to personalize machine learning models through an agile process. A novel white-box machine learning algorithm known as Data canyons was employed as a transparent and robust foundation for the proposed solution. By providing medical experts with a web framework where their expertise is transferred to a machine learning model and enabling the utilization of this process in an agile manner, a symbiotic relationship is fostered between the domains of medical expertise and machine learning. The flexibility to manipulate the output machine learning model and visually validate it, even without expertise in machine learning, establishes a crucial link between these two expert domains.
Keywords: XAI, explainable artificial intelligence, data canyons, machine learning, transparency, agile development, white-box model
Published in DKUM: 14.03.2024; Views: 94; Downloads: 4
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Data sharing concepts : a viable system model diagnosis
Igor Perko, 2023, original scientific article

Abstract: Purpose Artificial intelligence (AI) reasoning is fuelled by high-quality, detailed behavioural data. These can usually be obtained by the biometrical sensors embedded in smart devices. The currently used data collecting approach, where data ownership and property rights are taken by the data scientists, designers of a device or a related application, delivers multiple ethical, sociological and governance concerns. In this paper, the author is opening a systemic examination of a data sharing concept in which data producers execute their data property rights. Design/methodology/approach Since data sharing concept delivers a substantially different alternative, it needs to be thoroughly examined from multiple perspectives, among them: the ethical, social and feasibility. At this stage, theoretical examination modes in the form of literature analysis and mental model development are being performed. Findings Data sharing concepts, framework, mechanisms and swift viability are examined. The author determined that data sharing could lead to virtuous data science by augmenting data producers' capacity to govern their data and regulators' capacity to interact in the process. Truly interdisciplinary research is proposed to follow up on this research. Research limitations/implications Since the research proposal is theoretical, the proposal may not provide direct applicative value but is largely focussed on fuelling the research directions. Practical implications For the researchers, data sharing concepts will provide an alternative approach and help resolve multiple ethical considerations related to the internet of things (IoT) data collecting approach. For the practitioners in data science, it will provide numerous new challenges, such as distributed data storing, distributed data analysis and intelligent data sharing protocols. Social implications Data sharing may post significant implications in research and development. Since ethical, legislative moral and trust-related issues are managed in the negotiation process, data can be shared freely, which in a practical sense expands the data pool for virtuous research in social sciences. Originality/value The paper opens new research directions of data sharing concepts and space for a new field of research.
Keywords: hybrid reality, data sharing, systems thinking, cybernetics, artificial intelligence
Published in DKUM: 14.02.2024; Views: 223; Downloads: 6
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Multi-criteria measurement of ai support to project management
Vesna Čančer, Polona Tominc, Maja Rožman, 2023, original scientific article

Abstract: This paper aims to measure the level of artificial intelligence (AI) support to project management (PM) in selected service sector activities. The exploratory factor analysis was employed based on the extensive survey on AI in Slovenian companies and the multi-criteria measurement with an emphasis on value functions and pairwise comparisons in the analytic hierarchy process. The synthesis and performance sensitivity analysis results show that in the service sector, concerning all criteria, PM is with the level 0.276 best supported with AI in services of professional, scientific, and technical activities, which also stand out concerning the first-level goals in using AI solutions in a project with the value 0.284, and in successful project implementation using AI with the value 0.301. Although the lowest level of AI support to PM, which is 0.220, is in services of wholesale and retail trade and repair of motor vehicles and motorcycles, these services excel in adopting AI technologies in a project with a value of 0.277. Services of financial and insurance activities, with the level 0.257 second-ranked concerning all criteria, have the highest value of 0.269 concerning the first-level goal of improving the work of project leaders using AI. The paper, therefore, contributes to the comparison of AI support to PM in service sector activities. The results can help AI development policymakers determine which activities need to be supported and which should be set as an example. The presented methodological frame can serve to perform measurements and benchmarking in various research fields.
Keywords: artificial intelligence, factor analysis, multiple criteria, performance sensitivity, project management
Published in DKUM: 12.02.2024; Views: 175; Downloads: 12
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Assessing Perceived Trust and Satisfaction with Multiple Explanation Techniques in XAI-Enhanced Learning Analytics
Saša Brdnik, Vili Podgorelec, Boštjan Šumak, 2023, original scientific article

Abstract: This study aimed to observe the impact of eight explainable AI (XAI) explanation techniques on user trust and satisfaction in the context of XAI-enhanced learning analytics while comparing two groups of STEM college students based on their Bologna study level, using various established feature relevance techniques, certainty, and comparison explanations. Overall, the students reported the highest trust in local feature explanation in the form of a bar graph. Additionally, master's students presented with global feature explanations also reported high trust in this form of explanation. The highest measured explanation satisfaction was observed with the local feature explanation technique in the group of bachelor's and master's students, with master's students additionally expressing high satisfaction with the global feature importance explanation. A detailed overview shows that the two observed groups of students displayed consensus in favored explanation techniques when evaluating trust and explanation satisfaction. Certainty explanation techniques were perceived with lower trust and satisfaction than were local feature relevance explanation techniques. The correlation between itemized results was documented and measured with the Trust in Automation questionnaire and Explanation Satisfaction Scale questionnaire. Master's-level students self-reported an overall higher understanding of the explanations and higher overall satisfaction with explanations and perceived the explanations as less harmful.
Keywords: explainable artificial intelligence, learning analytics, XAI techniques, trust, explanation satisfaction
Published in DKUM: 12.02.2024; Views: 177; Downloads: 13
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Artificial-intelligence-supported reduction of employees’ workload to increase the company’s performance in today’s VUCA environment
Maja Rožman, Dijana Oreški, Polona Tominc, 2023, original scientific article

Abstract: This paper aims to develop a multidimensional model of AI-supported employee workload reduction to increase company performance in today's VUCA environment. Multidimensional constructs of the model include several aspects of artificial intelligence related to human resource management: AI-supported organizational culture, AI-supported leadership, AI-supported appropriate training and development of employees, employees' perceived reduction of their workload by AI, employee engagement, and company's performance. The main survey involved 317 medium-sized and large Slovenian companies. Structural equation modeling was used to test the hypotheses. The results show that three multidimensional constructs (AI-supported organizational culture, AI-supported leadership, and AI-supported appropriate training and development of employees) have a statistically significant positive effect on employees' perceived reduction of their workload by AI. In addition, employees' perceived reduced workload by AI has a statistically significant positive effect on employee engagement. The results show that employee engagement has a statistically significant positive effect on company performance. The concept of engagement is based on the fact that the development and growth of the company cannot be achieved by increasing the number of employees or by adding capital; the added value comes primarily from increased productivity, which is a result of the innovative ability of employees and their work engagement, which improve the company's performance. The results will significantly contribute to creating new views in the field of artificial intelligence and adopting important decisions in creating working conditions for employees in today's rapidly changing work environment.
Keywords: artificial intelligence, leadership, employee engagement, company performance
Published in DKUM: 02.02.2024; Views: 142; Downloads: 24
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The potential of ai-driven assistants in scaled agile software development
Vasilka Saklamaeva, Luka Pavlič, 2024, original scientific article

Abstract: Scaled agile development approaches are now used widely in modern software engineering, allowing businesses to improve teamwork, productivity, and product quality. The incorporation of artificial intelligence (AI) into scaled agile development methods (SADMs) has emerged as a potential strategy in response to the ongoing demand for simplified procedures and the increasing complexity of software projects. This paper explores the intersection of AI-driven assistants within the context of the scaled agile framework (SAFe) for large-scale software development, as it stands out as the most widely adopted framework. Our paper pursues three principal objectives: (1) an evaluation of the challenges and impediments encountered by organizations during the implementation of SADMs, (2) an assessment of the potential advantages stemming from the incorporation of AI in large-scale contexts, and (3) the compilation of aspects of SADMs that AI-driven assistants enhance. Through a comprehensive systematic literature review, we identified and described 18 distinct challenges that organizations confront. In the course of our research, we pinpointed seven benefits and five challenges associated with the implementation of AI in SADMs. These findings were systematically categorized based on their occurrence either within the development phase or the phases encompassing planning and control. Furthermore, we compiled a list of 15 different AI-driven assistants and tools, subjecting them to a more detailed examination, and employing them to address the challenges we uncovered during our research. One of the key takeaways from this paper is the exceptional versatility and effectiveness of AI-driven assistants, demonstrating their capability to tackle a broader spectrum of problems. In conclusion, this paper not only sheds light on the transformative potential of AI, but also provides invaluable insights for organizations aiming to enhance their agility and management capabilities.
Keywords: SAFe, scaled agile framework, AI, artificial intelligence, tools, assistants, agile, large-scale
Published in DKUM: 26.01.2024; Views: 126; Downloads: 19
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