@article{34703,
  abstract     = {{<jats:p>One of the many purposes for which social robots are designed is education, and there have been many attempts to systematize their potential in this field. What these attempts have in common is the recognition that learning can be supported in a variety of ways because a learner can be engaged in different activities that foster learning. Up to now, three roles have been proposed when designing these activities for robots: as a teacher or tutor, a learning peer, or a novice. Current research proposes that deciding in favor of one role over another depends on the content or preferred pedagogical form. However, the design of activities changes not only the content of learning, but also the nature of a human–robot social relationship. This is particularly important in language acquisition, which has been recognized as a social endeavor. The following review aims to specify the differences in human–robot social relationships when children learn language through interacting with a social robot. After proposing categories for comparing these different relationships, we review established and more specific, innovative roles that a robot can play in language-learning scenarios. This follows <jats:xref>Mead’s (1946)</jats:xref> theoretical approach proposing that social roles are performed in interactive acts. These acts are crucial for learning, because not only can they shape the social environment of learning but also engage the learner to different degrees. We specify the degree of engagement by referring to <jats:xref>Chi’s (2009)</jats:xref> progression of learning activities that range from active, constructive, toward interactive with the latter fostering deeper learning. Taken together, this approach enables us to compare and evaluate different human–robot social relationships that arise when applying a robot in a particular social role.</jats:p>}},
  author       = {{Rohlfing, Katharina and Altvater-Mackensen, Nicole and Caruana, Nathan and van den Berghe, Rianne and Bruno, Barbara and Tolksdorf, Nils Frederik and Hanulíková, Adriana}},
  issn         = {{2296-9144}},
  journal      = {{Frontiers in Robotics and AI}},
  keywords     = {{Artificial Intelligence, Computer Science Applications}},
  publisher    = {{Frontiers Media SA}},
  title        = {{{Social/dialogical roles of social robots in supporting children’s learning of language and literacy—A review and analysis of innovative roles}}},
  doi          = {{10.3389/frobt.2022.971749}},
  volume       = {{9}},
  year         = {{2022}},
}

@inproceedings{23779,
  abstract     = {{Produktentstehung (PE) bezieht sich auf den Prozess der Planung und Entwicklung eines Produkts sowie der damit verbundenen Dienstleistungen von der ersten Idee bis zur Herstellung und zum Vertrieb. Während dieses Prozesses gibt es zahlreiche Aufgaben, die von menschlichem Fachwissen abhängen und typischerweise von erfahrenen Experten übernommen werden. Da sich das Feld der Künstlichen Intelligenz (KI) immer weiterentwickelt und seinen Weg in den Fertigungssektor findet, gibt es viele Möglichkeiten für eine Anwendung von KI, um bei der Lösung der oben genannten Aufgaben zu helfen. In diesem Paper geben wir einen umfassenden Überblick über den aktuellen Stand der Technik des Einsatzes von KI in der PE. 
Im Detail analysieren wir 40 bestehende Surveys zu KI in der PE und 94 Case Studies, um herauszufinden, welche Bereiche der PE von der aktuellen Forschung in diesem Bereich vorrangig adressiert werden, wie ausgereift die diskutierten KI-Methoden sind und inwieweit datenzentrierte Ansätze in der aktuellen Forschung genutzt werden.}},
  author       = {{Bernijazov, Ruslan and Dicks, Alexander and Dumitrescu, Roman and Foullois, Marc and Hanselle, Jonas Manuel and Hüllermeier, Eyke and Karakaya, Gökce and Ködding, Patrick and Lohweg, Volker and Malatyali, Manuel and Meyer auf der Heide, Friedhelm and Panzner, Melina and Soltenborn, Christian}},
  booktitle    = {{Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21)}},
  keywords     = {{Artificial Intelligence Product Creation Literature Review}},
  location     = {{Montreal, Kanada}},
  title        = {{{A Meta-Review on Artificial Intelligence in Product Creation}}},
  year         = {{2021}},
}

@article{37155,
  abstract     = {{Artificial intelligence (AI) has moved beyond the planning phase in many organisations and it is often accompanied by uncertainties and fears of job loss among employees. It is crucial to manage employees{\textquoteright} attitudes towards the deployment of an AI-based technology effectively and counteract possible resistance behaviour. We present lessons learned from an industry case where we conducted interviews with affected employees. We evaluated our results with managers across industries and found that that the deployment of AI-based technologies does not differ from other IT, but that the change is perceived differently due to misguided expectations. }},
  author       = {{Stieglitz, Stefan and Möllmann (Frick), Nicholas R. J. and Mirbabaie, Milad and Hofeditz, Lennart and Ross, Björn}},
  issn         = {{1477-9064}},
  journal      = {{International Journal of Management Practice}},
  keywords     = {{Artificial Intelligence, Change Management, Resistance, AI-Driven Change, AI Deployment, AI Perception}},
  publisher    = {{Inderscience}},
  title        = {{{Recommendations for Managing AI-Driven Change Processes: When Expectations Meet Reality}}},
  year         = {{2021}},
}

@article{30114,
  author       = {{Gölz, Christian Johannes and Mora, K. and Rudisch, J. and Gaidai, Roman and Reuter, E. and Godde, B. and Reinsberger, Claus and Voelcker-Rehage, C. and Vieluf, S.}},
  issn         = {{0893-6080}},
  journal      = {{Neural Networks}},
  keywords     = {{Artificial Intelligence, Cognitive Neuroscience}},
  pages        = {{363--374}},
  publisher    = {{Elsevier BV}},
  title        = {{{Classification of visuomotor tasks based on electroencephalographic data depends on age-related differences in brain activity patterns}}},
  doi          = {{10.1016/j.neunet.2021.04.029}},
  volume       = {{142}},
  year         = {{2021}},
}

@article{31400,
  author       = {{Goelz, C. and Mora, K. and Rudisch, J. and Gaidai, R. and Reuter, E. and Godde, B. and Reinsberger, Claus and Voelcker-Rehage, C. and Vieluf, S.}},
  issn         = {{0893-6080}},
  journal      = {{Neural Networks}},
  keywords     = {{Artificial Intelligence, Cognitive Neuroscience}},
  pages        = {{363--374}},
  publisher    = {{Elsevier BV}},
  title        = {{{Classification of visuomotor tasks based on electroencephalographic data depends on age-related differences in brain activity patterns}}},
  doi          = {{10.1016/j.neunet.2021.04.029}},
  volume       = {{142}},
  year         = {{2021}},
}

@inproceedings{27491,
  abstract     = {{ Students often have a lack of understanding and awareness of where, how, and why personal data about them is collected and processed. Especially, when interacting with data-driven digital artifacts, an appropriate perception of the data collection and processing is necessary for self-determination. This dissertation deals with the development and evaluation of a concept called data awareness which aims to foster students’ self-determination interacting with data-driven digital artifacts.}},
  author       = {{Höper, Lukas}},
  booktitle    = {{21st Koli Calling International Conference on Computing Education Research}},
  isbn         = {{9781450384889}},
  keywords     = {{data awareness, machine learning, data science education, data-driven digital artifacts, artificial intelligence}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{Developing and Evaluating the Concept Data Awareness for K12 Computing Education}}},
  doi          = {{10.1145/3488042.3490509}},
  year         = {{2021}},
}

@inproceedings{15332,
  abstract     = {{Artificial intelligence (AI) has the potential for far-reaching – in our opinion – irreversible changes.
They range from effects on the individual and society to new societal and social issues. The question arises
as to how students can learn the basic functioning of AI systems, what areas of life and society are affected
by these and – most important – how their own lives are affected by these changes. Therefore, we are developing and evaluating school materials for the German ”Science Year AI”. It can be used for students of all
school types from the seventh grade upwards and will be distributed to about 2000 schools in autumn with
the support of the Federal Ministry of Education and Research. The material deals with the following aspects
of AI: Discussing everyday experiences with AI, how does machine learning work, historical development
of AI concepts, difference between man and machine, future distribution of roles between man and machine,
in which AI world do we want to live and how much AI would we like to have in our lives. Through an
accompanying evaluation, high quality of the technical content and didactic preparation is achieved in order
to guarantee the long-term applicability in the teaching context in the different age groups and school types.
In this paper, we describe the current state of the material development, the challenges arising, and the results
of tests with different classes to date. We also present first ideas for evaluating the results.}},
  author       = {{Schlichtig, Michael and Opel, Simone Anna and Budde, Lea and Schulte, Carsten}},
  booktitle    = {{ISSEP 2019 - 12th International conference on informatics in schools: Situation, evaluation and perspectives, Local Proceedings}},
  editor       = {{Jasutė, Eglė and Pozdniakov, Sergei}},
  isbn         = {{978-9925-553-27-3}},
  keywords     = {{Artificial Intelligence, Machine Learning, Teaching Material, Societal Aspects, Ethics. Social Aspects, Science Year, Simulation Game}},
  location     = {{Lanarca}},
  pages        = {{65 -- 73}},
  title        = {{{Understanding Artificial Intelligence – A Project for the Development of Comprehensive Teaching Material}}},
  volume       = {{12}},
  year         = {{2019}},
}

@article{42677,
  author       = {{Klowait, Nils}},
  issn         = {{0951-5666}},
  journal      = {{AI & SOCIETY}},
  keywords     = {{Artificial Intelligence, Human-Computer Interaction, Philosophy}},
  number       = {{4}},
  pages        = {{527--536}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{The quest for appropriate models of human-likeness: anthropomorphism in media equation research}}},
  doi          = {{10.1007/s00146-017-0746-z}},
  volume       = {{33}},
  year         = {{2017}},
}

@article{48306,
  abstract     = {{<jats:p>The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. (ii) We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. (iii) We create a new gold standard corpus (90k tokens in 340 documents) and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task.</jats:p>}},
  author       = {{Habernal, Ivan and Gurevych, Iryna}},
  issn         = {{0891-2017}},
  journal      = {{Computational Linguistics}},
  keywords     = {{Artificial Intelligence, Computer Science Applications, Linguistics and Language, Language and Linguistics}},
  number       = {{1}},
  pages        = {{125--179}},
  publisher    = {{MIT Press}},
  title        = {{{Argumentation Mining in User-Generated Web Discourse}}},
  doi          = {{10.1162/coli_a_00276}},
  volume       = {{43}},
  year         = {{2016}},
}

@article{52747,
  author       = {{Borgwardt, Stefan and Mailis, Theofilos and Peñaloza, Rafael and Turhan, Anni-Yasmin}},
  issn         = {{1861-2032}},
  journal      = {{Journal on Data Semantics}},
  keywords     = {{Artificial Intelligence, Computer Networks and Communications, Information Systems}},
  number       = {{2}},
  pages        = {{55--75}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Answering Fuzzy Conjunctive Queries Over Finitely Valued Fuzzy Ontologies}}},
  doi          = {{10.1007/s13740-015-0055-y}},
  volume       = {{5}},
  year         = {{2016}},
}

@article{52803,
  author       = {{Borgwardt, Stefan and Mailis, Theofilos and Peñaloza, Rafael and Turhan, Anni-Yasmin}},
  issn         = {{1861-2032}},
  journal      = {{Journal on Data Semantics}},
  keywords     = {{Artificial Intelligence, Computer Networks and Communications, Information Systems}},
  number       = {{2}},
  pages        = {{55--75}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Answering Fuzzy Conjunctive Queries Over Finitely Valued Fuzzy Ontologies}}},
  doi          = {{10.1007/s13740-015-0055-y}},
  volume       = {{5}},
  year         = {{2016}},
}

@article{50438,
  author       = {{Baraté, Adriano and Haus, Goffredo and Ludovico, Luca Andrea and Mauro, Davide Andrea}},
  issn         = {{1796-2048}},
  journal      = {{Journal of Multimedia}},
  keywords     = {{Electrical and Electronic Engineering, Artificial Intelligence, Media Technology}},
  number       = {{2}},
  publisher    = {{Academy Publisher}},
  title        = {{{IEEE 1599 for Live Musical and Theatrical Performances}}},
  doi          = {{10.4304/jmm.7.2.170-178}},
  volume       = {{7}},
  year         = {{2012}},
}

@inproceedings{9736,
  abstract     = {{Self-optimizing mechatronic systems are a new class of technical systems. On the one hand, new challenges regarding dependability arise from their additional complexity and adaptivity. On the other hand, their abilities enable new concepts and methods to improve the dependability of mechatronic systems. This paper introduces a multi-level dependability concept for self-optimizing mechatronic systems and shows how planning can be used to improve the availability and reliability of systems in the operating stages.}},
  author       = {{Klöpper, Benjamin and Sondermann-Wölke, Christoph and Romaus, Christoph and Vöcking, Henner}},
  booktitle    = {{Computational Intelligence in Control and Automation, 2009. CICA 2009. IEEE Symposium on}},
  keywords     = {{multilevel dependability concept, probabilistic planning, self-optimizing mechatronic systems, systems reliability, mechatronics, planning (artificial intelligence), self-adjusting systems}},
  pages        = {{104 --111}},
  title        = {{{Probabilistic planning integrated in a multi-level dependability concept for mechatronic systems}}},
  doi          = {{10.1109/CICA.2009.4982790}},
  year         = {{2009}},
}