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1.
Science teachers’ approach to contemporary assessment with a reading literacy emphasis
Maja Kerneža, Dejan Zemljak, 2023, izvirni znanstveni članek

Opis: In a sample of 1215 teachers, this study examined the readiness of science educators for assessment in the rapidly evolving landscape of artificial intelligence in education. Participants responded to an online questionnaire during the emergency remote teaching phase, offering insights into the frequency and nature of assessment methods utilized. The research draws a connection between assessment techniques during remote teaching and the emergence of AI in education. The results show that the selected assessment methods vary across teachers, with some specific differences observed in the assessment practices of science teachers. The study underscores the critical role of reading literacy in enhancing student engagement in contemporary learning environments. Moreover, the findings suggest that continuous professional development significantly improves the readiness of (science) teachers for AI-enhanced assessment. Drawing from these insights, recommendations for subsequent research are delineated.
Ključne besede: artificial intelligence, assessment, reading literacy, science teachers, teacher training
Objavljeno v DKUM: 08.05.2024; Ogledov: 33; Prenosov: 2
.pdf Celotno besedilo (1,08 MB)
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2.
Tool condition monitoring using machine tool spindle current and long short-term memory neural network model analysis
Niko Turšič, Simon Klančnik, 2024, izvirni znanstveni članek

Opis: In cutting processes, tool condition affects the quality of the manufactured parts. As such, an essential component to prevent unplanned downtime and to assure machining quality is having information about the state of the cutting tool. The primary function of it is to alert the operator that the tool has reached or is reaching a level of wear beyond which behaviour is unreliable. In this paper, the tool condition is being monitored by analysing the electric current on the main spindle via an artificial intelligence model utilising an LSTM neural network. In the current study, the tool is monitored while working on a cylindrical raw piece made of AA6013 aluminium alloy with a custom polycrystalline diamond tool for the purposes of monitoring the wear of these tools. Spindle current characteristics were obtained using external measuring equipment to not influence the operation of the machine included in a larger production line. As a novel approach, an artificial intelligence model based on an LSTM neural network is utilised for the analysis of the spindle current obtained during a manufacturing cycle and assessing the tool wear range in real time. The neural network was designed and trained to notice significant characteristics of the captured current signal. The conducted research serves as a proof of concept for the use of an LSTM neural network-based model as a method of monitoring the condition of cutting tools.
Ključne besede: tool condition monitoring, artificial intelligence, LSTM neural network
Objavljeno v DKUM: 22.04.2024; Ogledov: 63; Prenosov: 8
.pdf Celotno besedilo (3,75 MB)
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3.
Smart education systems supported by ICT and AI
Boris Aberšek, Andrej Flogie, 2023, predgovor, uvodnik, spremna beseda

Ključne besede: teaching and learning, smart education systems, education and technology, ICT, artificial intelligence
Objavljeno v DKUM: 05.04.2024; Ogledov: 179; Prenosov: 2
.pdf Celotno besedilo (175,31 KB)
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4.
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, pregledni znanstveni članek

Opis: 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.
Ključne besede: biomedical and health informatics, artificial intelligence in medicine, research trends, CoViD-19
Objavljeno v DKUM: 03.04.2024; Ogledov: 219; Prenosov: 233
.pdf Celotno besedilo (1,73 MB)
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5.
Artificial intelligence and business studies : study cycle differences regarding the perceptions of the key future competences
Polona Tominc, Maja Rožman, 2023, izvirni znanstveni članek

Opis: 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.
Ključne besede: artificial intelligence, undergraduate students, postgraduate students, education
Objavljeno v DKUM: 03.04.2024; Ogledov: 100; Prenosov: 10
.pdf Celotno besedilo (319,78 KB)
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6.
Artificial intelligence and agility-based model for successful project implementation and company competitiveness
Polona Tominc, Dijana Oreški, Maja Rožman, 2023, izvirni znanstveni članek

Opis: 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.
Ključne besede: artificial intelligence, agile work environment, company competitiveness, project management
Objavljeno v DKUM: 26.03.2024; Ogledov: 177; Prenosov: 162
.pdf Celotno besedilo (1,68 MB)
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7.
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, izvirni znanstveni članek

Opis: 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.
Ključne besede: XAI, explainable artificial intelligence, data canyons, machine learning, transparency, agile development, white-box model
Objavljeno v DKUM: 14.03.2024; Ogledov: 163; Prenosov: 14
.pdf Celotno besedilo (5,28 MB)
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8.
Data sharing concepts : a viable system model diagnosis
Igor Perko, 2023, izvirni znanstveni članek

Opis: 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.
Ključne besede: hybrid reality, data sharing, systems thinking, cybernetics, artificial intelligence
Objavljeno v DKUM: 14.02.2024; Ogledov: 269; Prenosov: 6
.pdf Celotno besedilo (663,49 KB)
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9.
Multi-criteria measurement of ai support to project management
Vesna Čančer, Polona Tominc, Maja Rožman, 2023, izvirni znanstveni članek

Opis: 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.
Ključne besede: artificial intelligence, factor analysis, multiple criteria, performance sensitivity, project management
Objavljeno v DKUM: 12.02.2024; Ogledov: 247; Prenosov: 15
.pdf Celotno besedilo (4,18 MB)
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10.
Assessing Perceived Trust and Satisfaction with Multiple Explanation Techniques in XAI-Enhanced Learning Analytics
Saša Brdnik, Vili Podgorelec, Boštjan Šumak, 2023, izvirni znanstveni članek

Opis: 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.
Ključne besede: explainable artificial intelligence, learning analytics, XAI techniques, trust, explanation satisfaction
Objavljeno v DKUM: 12.02.2024; Ogledov: 257; Prenosov: 17
.pdf Celotno besedilo (3,24 MB)
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