@inproceedings{62885,
  author       = {{Osnabrügge, Malin and Tenberge, Claudia and Fechner, Sabine}},
  keywords     = {{Artificial intelligence, primary education, science and technology education}},
  location     = {{Norrköping, Sweden}},
  title        = {{{Artificial Intelligence in primary science and technology education with a focus on implementation of AI in learning context – Results of a Scoping Review}}},
  year         = {{2026}},
}

@article{58076,
  abstract     = {{This paper presents the concept of Information Circularity Assistance, which provides decision support in the early stages of product creation for Circular Economy. Engineers in strategic product planning need to proactively predict the quantity, quality, and timing of secondary materials and returned components. For example, products with high recycled content will only be economically sustainable if the material is actually available in the future product life. Our assumption is that Information Circularity Assistance enables decision makers to incorporate insights from extreme data – high-volume, high-velocity, heterogeneous and distributed data from the product life – into product creation through intelligent Digital Twins. Artificial Intelligence can help to derive sustainable actions in favor of circular products by processing extreme data and enriching it with expert knowledge. The research contributes in three key dimensions. First, a comprehensive literature review is conducted. This review covers concepts of intelligence in Scenario-Technique for strategic product planning, Digital Twin-based analysis of extreme data and relevant technologies from Data Science and Artificial Intelligence. In all areas, the state of the art and emerging trends are identified. Secondly, the study identifies information needs along the steps of the Scenario-Technique and information offerings based on Digital Twins. The concept of Information Circularity Assistance results from the coupling of these demands and offerings, extending the Scenario-Technique beyond traditional expert-based methods. Third, we extend existing Digital Twin methods used in circularity and discuss the deployment of Data Science and Artificial Intelligence algorithms within the product creation process. Our approach uses extreme data to provide a strategic advantage in optimizing product life cycle planning, which is illustrated by two sample applications. The aim is to provide Information Circularity Assistance that will support experienced product planners, developers, and decision makers in the future.}},
  author       = {{Gräßler, Iris and Weyrich, Michael and Pottebaum, Jens and Kamm, Simon}},
  issn         = {{0178-2312}},
  journal      = {{at - Automatisierungstechnik}},
  keywords     = {{Scenario-Technique, Artificial Intelligence, Digital Twin, Large Language Models}},
  number       = {{1}},
  pages        = {{3--21}},
  publisher    = {{Walter de Gruyter GmbH}},
  title        = {{{Information Circularity Assistance based on extreme data}}},
  doi          = {{10.1515/auto-2024-0039}},
  volume       = {{73}},
  year         = {{2025}},
}

@article{58650,
  abstract     = {{Technical systems are characterized by increasing interdisciplinarity, complexity and networking. A product and its corresponding production systems require interdisciplinary multi-objective optimization. Sustainability and recyclability demands increase said complexity. The efficiency of previously established engineering methods is reaching its limits, which can only be overcome by systematic integration of extreme data. The aim of "hybrid decision support" is as follows: Data science and artificial intelligence should be used to supplement human capabilities in conjunction with existing heuristics, methods, modeling and simulation to increase the efficiency of product creation.}},
  author       = {{Gräßler, Iris and Pottebaum, Jens and Nyhuis, Peter and Stark, Rainer and Thoben, Klaus-Dieter and Wiederkehr, Petra}},
  issn         = {{2942-6170}},
  journal      = {{Industry 4.0 Science}},
  keywords     = {{AI, artificial intelligence, Data Science, decision support, extreme data, Künstliche Intelligenz, product creation, product development}},
  number       = {{1}},
  publisher    = {{GITO mbH Verlag}},
  title        = {{{Hybrid Decision Support in Product Creation - Improving performance with data science and artificial intelligence}}},
  doi          = {{10.30844/i4sd.25.1.18}},
  volume       = {{2025}},
  year         = {{2025}},
}

@inproceedings{62920,
  author       = {{Fox, Marvin Lee and Peeters, Hendrik and Fechner, Sabine}},
  booktitle    = {{GDCP Jahrestagung}},
  keywords     = {{Artificial intelligence, education, chemistry}},
  location     = {{Frankfurt}},
  title        = {{{KI-Einsatz durch Lernende im Erkenntnisgewinnungsprozess - ein Review}}},
  year         = {{2025}},
}

@article{63053,
  author       = {{Hernández, Carlos and Rodriguez-Fernandez, Angel E. and Schäpermeier, Lennart and Cuate, Oliver and Trautmann, Heike and Schütze, Oliver}},
  journal      = {{IEEE Transactions on Evolutionary Computation}},
  keywords     = {{Optimization, Evolutionary computation, Hands, Proposals, Convergence, Computational efficiency, Artificial intelligence, Accuracy, Approximation algorithms, Aerospace electronics, Multi-objective optimization, evolutionary algorithms, nearly optimal solutions, multimodal optimization, archiving, continuation}},
  pages        = {{1--1}},
  title        = {{{An Evolutionary Approach for the Computation of ∈-Locally Optimal Solutions for Multi-Objective Multimodal Optimization}}},
  doi          = {{10.1109/TEVC.2025.3637276}},
  year         = {{2025}},
}

@inproceedings{62921,
  author       = {{Fox, Marvin Lee and Peeters, Hendrik and Fechner, Sabine}},
  booktitle    = {{Conference of The European Science Education Research Association (ESERA)}},
  keywords     = {{Artificial intelligence, education, chemistry}},
  location     = {{Copenhagen, Denmark}},
  title        = {{{How can students be supported by ChatGPT as a tutor in hands-on chemistry education?}}},
  year         = {{2025}},
}

@article{53213,
  author       = {{Amiri, Arman and Tavana, Madjid and Arman, Hosein}},
  issn         = {{2542-6605}},
  journal      = {{Internet of Things}},
  keywords     = {{Management of Technology and Innovation, Artificial Intelligence, Computer Science Applications, Hardware and Architecture, Engineering (miscellaneous), Information Systems, Computer Science (miscellaneous), Software}},
  publisher    = {{Elsevier BV}},
  title        = {{{An Integrated Fuzzy Analytic Network Process and Fuzzy Regression Method for Bitcoin Price Prediction}}},
  doi          = {{10.1016/j.iot.2023.101027}},
  volume       = {{25}},
  year         = {{2024}},
}

@inproceedings{56166,
  abstract     = {{Developing Intelligent Technical Systems (ITS) involves a complex process encompassing planning, analysis, design, production, and maintenance. Model-Based Systems Engineering (MBSE) is a key methodology for systematic systems engineering. Designing models for ITS requires harmonious interaction of various elements, posing a challenge in MBSE. Leveraging Generative Artificial Intelligence, we generated a dataset for modeling, using prompt engineering on large language models. The generated artifacts can aid engineers in MBSE design or serve as synthetic training data for AI assistants.}},
  author       = {{Kulkarni, Pranav Jayant and Tissen, Denis and Bernijazov, Ruslan and Dumitrescu, Roman}},
  booktitle    = {{DS 130: Proceedings of NordDesign 2024}},
  editor       = {{Malmqvist, J. and Candi, M. and Saemundsson, R. and Bystrom, F. and Isaksson, O.}},
  keywords     = {{Data Driven Design, Design Automation, Systems Engineering (SE), Artificial Intelligence (AI)}},
  location     = {{Reykjavik}},
  pages        = {{617--625}},
  title        = {{{Towards Automated Design: Automatically Generating Modeling Elements with Prompt Engineering and Generative Artificial Intelligence}}},
  doi          = {{10.35199/NORDDESIGN2024.66}},
  year         = {{2024}},
}

@inproceedings{53073,
  abstract     = {{While shallow decision trees may be interpretable, larger ensemble models like gradient-boosted trees, which often set the state of the art in machine learning problems involving tabular data, still remain black box models. As a remedy, the Shapley value (SV) is a well-known concept in explainable artificial intelligence (XAI) research for quantifying additive feature attributions of predictions. The model-specific TreeSHAP methodology solves the exponential complexity for retrieving exact SVs from tree-based models. Expanding beyond individual feature attribution, Shapley interactions reveal the impact of intricate feature interactions of any order. In this work, we present TreeSHAP-IQ, an efficient method to compute any-order additive Shapley interactions for predictions of tree-based models. TreeSHAP-IQ is supported by a mathematical framework that exploits polynomial arithmetic to compute the interaction scores in a single recursive traversal of the tree, akin to Linear TreeSHAP. We apply TreeSHAP-IQ on state-of-the-art tree ensembles and explore interactions on well-established benchmark datasets.}},
  author       = {{Muschalik, Maximilian and Fumagalli, Fabian and Hammer, Barbara and Huellermeier, Eyke}},
  booktitle    = {{Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)}},
  issn         = {{2374-3468}},
  keywords     = {{Explainable Artificial Intelligence}},
  number       = {{13}},
  pages        = {{14388--14396}},
  title        = {{{Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles}}},
  doi          = {{10.1609/aaai.v38i13.29352}},
  volume       = {{38}},
  year         = {{2024}},
}

@article{48290,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>Identifying, classifying, and analyzing arguments in legal discourse has been a prominent area of research since the inception of the argument mining field. However, there has been a major discrepancy between the way natural language processing (NLP) researchers model and annotate arguments in court decisions and the way legal experts understand and analyze legal argumentation. While computational approaches typically simplify arguments into generic premises and claims, arguments in legal research usually exhibit a rich typology that is important for gaining insights into the particular case and applications of law in general. We address this problem and make several substantial contributions to move the field forward. First, we design a new annotation scheme for legal arguments in proceedings of the European Court of Human Rights (ECHR) that is deeply rooted in the theory and practice of legal argumentation research. Second, we compile and annotate a large corpus of 373 court decisions (2.3M tokens and 15k annotated argument spans). Finally, we train an argument mining model that outperforms state-of-the-art models in the legal NLP domain and provide a thorough expert-based evaluation. All datasets and source codes are available under open lincenses at <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://github.com/trusthlt/mining-legal-arguments">https://github.com/trusthlt/mining-legal-arguments</jats:ext-link>.</jats:p>}},
  author       = {{Habernal, Ivan and Faber, Daniel and Recchia, Nicola and Bretthauer, Sebastian and Gurevych, Iryna and Spiecker genannt Döhmann, Indra and Burchard, Christoph}},
  issn         = {{0924-8463}},
  journal      = {{Artificial Intelligence and Law}},
  keywords     = {{Law, Artificial Intelligence}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Mining legal arguments in court decisions}}},
  doi          = {{10.1007/s10506-023-09361-y}},
  year         = {{2023}},
}

@article{48777,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>Explainable artificial intelligence has mainly focused on static learning scenarios so far. We are interested in dynamic scenarios where data is sampled progressively, and learning is done in an incremental rather than a batch mode. We seek efficient incremental algorithms for computing feature importance (FI). Permutation feature importance (PFI) is a well-established model-agnostic measure to obtain global FI based on feature marginalization of absent features. We propose an efficient, model-agnostic algorithm called iPFI to estimate this measure incrementally and under dynamic modeling conditions including concept drift. We prove theoretical guarantees on the approximation quality in terms of expectation and variance. To validate our theoretical findings and the efficacy of our approaches in incremental scenarios dealing with streaming data rather than traditional batch settings, we conduct multiple experimental studies on benchmark data with and without concept drift.</jats:p>}},
  author       = {{Fumagalli, Fabian and Muschalik, Maximilian and Hüllermeier, Eyke and Hammer, Barbara}},
  issn         = {{0885-6125}},
  journal      = {{Machine Learning}},
  keywords     = {{Artificial Intelligence, Software}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Incremental permutation feature importance (iPFI): towards online explanations on data streams}}},
  doi          = {{10.1007/s10994-023-06385-y}},
  year         = {{2023}},
}

@article{44639,
  author       = {{Hoppe, Julia Amelie and Tuisku, Outi and Johansson-Pajala, Rose-Marie and Pekkarinen, Satu and Hennala, Lea and Gustafsson, Christine and Melkas, Helinä and Thommes, Kirsten}},
  issn         = {{2451-9588}},
  journal      = {{Computers in Human Behavior Reports}},
  keywords     = {{Artificial Intelligence, Cognitive Neuroscience, Computer Science Applications, Human-Computer Interaction, Applied Psychology, Neuroscience (miscellaneous)}},
  publisher    = {{Elsevier BV}},
  title        = {{{When do individuals choose care robots over a human caregiver? Insights from a laboratory experiment on choices under uncertainty}}},
  doi          = {{10.1016/j.chbr.2022.100258}},
  volume       = {{9}},
  year         = {{2023}},
}

@article{49516,
  abstract     = {{<jats:p>In this article, we present RISE—a <jats:bold>R</jats:bold>obotics <jats:bold>I</jats:bold>ntegration and <jats:bold>S</jats:bold>cenario-Management <jats:bold>E</jats:bold>xtensible-Architecture—for designing human–robot dialogs and conducting <jats:italic>Human–Robot Interaction</jats:italic> (HRI) studies. In current HRI research, interdisciplinarity in the creation and implementation of interaction studies is becoming increasingly important. In addition, there is a lack of reproducibility of the research results. With the presented open-source architecture, we aim to address these two topics. Therefore, we discuss the advantages and disadvantages of various existing tools from different sub-fields within robotics. Requirements for an architecture can be derived from this overview of the literature, which 1) supports interdisciplinary research, 2) allows reproducibility of the research, and 3) is accessible to other researchers in the field of HRI. With our architecture, we tackle these requirements by providing a <jats:italic>Graphical User Interface</jats:italic> which explains the robot behavior and allows introspection into the current state of the dialog. Additionally, it offers controlling possibilities to easily conduct <jats:italic>Wizard of Oz</jats:italic> studies. To achieve transparency, the dialog is modeled explicitly, and the robot behavior can be configured. Furthermore, the modular architecture offers an interface for external features and sensors and is expandable to new robots and modalities.</jats:p>}},
  author       = {{Groß, André and Schütze, Christian and Brandt, Mara and Wrede, Britta and Richter, Birte}},
  issn         = {{2296-9144}},
  journal      = {{Frontiers in Robotics and AI}},
  keywords     = {{Artificial Intelligence, Computer Science Applications}},
  publisher    = {{Frontiers Media SA}},
  title        = {{{RISE: an open-source architecture for interdisciplinary and reproducible human–robot interaction research}}},
  doi          = {{10.3389/frobt.2023.1245501}},
  volume       = {{10}},
  year         = {{2023}},
}

@inproceedings{50121,
  abstract     = {{Many researchers and practitioners see artificial intelligence as a game changer compared to classical statistical models. However, some software providers engage in “AI washing”, relabeling solutions that use simple statistical models as AI systems. By contrast, research on algorithm aversion unsystematically varied the labels for advisors and treated labels such as "artificial intelligence" and "statistical model" synonymously. This study investigates the effect of individual labels on users' actual advice utilization behavior. Through two incentivized online within-subjects experiments on regression tasks, we find that labeling human advisors with labels that suggest higher expertise leads to an increase in advice-taking, even though the content of the advice remains the same. In contrast, our results do not suggest such an expert effect for advice-taking from algorithms, despite differences in self-reported perception. These findings challenge the effectiveness of framing intelligent systems as AI-based systems and have important implications for both research and practice.}},
  author       = {{Leffrang, Dirk}},
  booktitle    = {{International Conference on Information Systems}},
  keywords     = {{Artificial Intelligence, Algorithm Appreciation, Framing, Advice-taking, Expertise}},
  location     = {{Hyderabad, India}},
  number       = {{10}},
  title        = {{{AI Washing: The Framing Effect of Labels on Algorithmic Advice Utilization}}},
  year         = {{2023}},
}

@article{53301,
  author       = {{Vieluf, Solveig and Hasija, Tanuj and Kuschel, Maurice and Reinsberger, Claus and Loddenkemper, Tobias}},
  issn         = {{0957-4174}},
  journal      = {{Expert Systems with Applications}},
  keywords     = {{Artificial Intelligence, Computer Science Applications, General Engineering}},
  publisher    = {{Elsevier BV}},
  title        = {{{Developing a deep canonical correlation-based technique for seizure prediction}}},
  doi          = {{10.1016/j.eswa.2023.120986}},
  volume       = {{234}},
  year         = {{2023}},
}

@article{53220,
  author       = {{Tavana, Madjid and Khalili Nasr, Arash and Ahmadabadi, Alireza Barati and Amiri, Alireza Shamekhi and Mina, Hassan}},
  issn         = {{2542-6605}},
  journal      = {{Internet of Things}},
  keywords     = {{Management of Technology and Innovation, Artificial Intelligence, Computer Science Applications, Hardware and Architecture, Engineering (miscellaneous), Information Systems, Computer Science (miscellaneous), Software}},
  publisher    = {{Elsevier BV}},
  title        = {{{An interval multi-criteria decision-making model for evaluating blockchain-IoT technology in supply chain networks}}},
  doi          = {{10.1016/j.iot.2023.100786}},
  volume       = {{22}},
  year         = {{2023}},
}

@article{53218,
  author       = {{Tavana, Madjid and Soltanifar, Mehdi and Santos-Arteaga, Francisco J. and Sharafi, Hamid}},
  issn         = {{0957-4174}},
  journal      = {{Expert Systems with Applications}},
  keywords     = {{Artificial Intelligence, Computer Science Applications, General Engineering}},
  publisher    = {{Elsevier BV}},
  title        = {{{Analytic hierarchy process and data envelopment analysis: A match made in heaven}}},
  doi          = {{10.1016/j.eswa.2023.119902}},
  volume       = {{223}},
  year         = {{2023}},
}

@article{53229,
  author       = {{Santos-Arteaga, Francisco J. and Di Caprio, Debora and Tavana, Madjid and Tena, Emilio Cerda}},
  issn         = {{1063-6706}},
  journal      = {{IEEE Transactions on Fuzzy Systems}},
  keywords     = {{Applied Mathematics, Artificial Intelligence, Computational Theory and Mathematics, Control and Systems Engineering}},
  number       = {{2}},
  pages        = {{460--474}},
  publisher    = {{Institute of Electrical and Electronics Engineers (IEEE)}},
  title        = {{{A Credibility and Strategic Behavior Approach in Hesitant Multiple Criteria Decision-Making With Application to Sustainable Transportation}}},
  doi          = {{10.1109/tfuzz.2022.3188875}},
  volume       = {{31}},
  year         = {{2023}},
}

@article{53228,
  author       = {{Tirkolaee, Erfan Babaee and Torkayesh, Ali Ebadi and Tavana, Madjid and Goli, Alireza and Simic, Vladimir and Ding, Weiping}},
  issn         = {{0952-1976}},
  journal      = {{Engineering Applications of Artificial Intelligence}},
  keywords     = {{Electrical and Electronic Engineering, Artificial Intelligence, Control and Systems Engineering}},
  publisher    = {{Elsevier BV}},
  title        = {{{An integrated decision support framework for resilient vaccine supply chain network design}}},
  doi          = {{10.1016/j.engappai.2023.106945}},
  volume       = {{126}},
  year         = {{2023}},
}

@article{53230,
  author       = {{Mahdiraji, Hannan Amoozad and Tavana, Madjid and Rezayar, Ali}},
  issn         = {{0196-9722}},
  journal      = {{Cybernetics and Systems}},
  keywords     = {{Artificial Intelligence, Information Systems, Software}},
  number       = {{1}},
  pages        = {{104--137}},
  publisher    = {{Informa UK Limited}},
  title        = {{{A Game-Theoretic Framework for Analyzing the Impact of Social Responsibility and Supply Chain Profitability}}},
  doi          = {{10.1080/01969722.2022.2055402}},
  volume       = {{54}},
  year         = {{2023}},
}