TY - CHAP A1 - Jung, Joshua A1 - Morgan, Jonathan A1 - Hoey, Jesse A1 - Schröder, Tobias A1 - Wolf, Ingo T1 - Grounding social interaction with affective intelligence T2 - Advances in Artificial Intelligence : 29th Canadian Conference on Artificial Intelligence, Canadian AI 2016, Victoria, BC, Canada, May 31 - June 3, 2016. Proceedings KW - Künstliche Intelligenz KW - Soziologie KW - Humanfaktor KW - Informationsverhalten Y1 - 2016 SN - 978-3-319-34111-8 U6 - https://doi.org/10.1007/978-3-319-34111-8_7 SP - 52 EP - 57 PB - Springer CY - Cham ER - TY - CHAP A1 - Hoey, Jesse A1 - Schröder, Tobias T1 - Bayesian affect control theory of self T2 - Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence and the Twenty-Seventh Innovative Applications of Artificial Intelligence Conference, 25 - 30 January, Austin, Texas, USA. Volume 1 N2 - Notions of identity and of the self have long been studied in social psychology and sociology as key guiding elements of social interaction and coordination. In the AI of the future, these notions will also play a role in producing natural, socially appropriate artificially intelligent agents that encompass subtle and complex human social and affective skills. We propose here a Bayesian generalization of the sociological affect control theory of self as a theoretical foundation for socio-affectively skilled artificial agents. This theory posits that each human maintains an internal model of his or her deep sense of "self" that captures their emotional, psychological, and socio-cultural sense of being in the world. The "self" is then externalised as an identity within any given interpersonal and institutional situation, and this situational identity is the person's local (in space and time) representation of the self. Situational identities govern the actions of humans according to affect control theory. Humans will seek situations that allow them to enact identities consistent with their sense of self. This consistency is cumulative over time: if some parts of a person's self are not actualized regularly, the person will have a growing feeling of inauthenticity that they will seek to resolve. In our present generalisation, the self is represented as a probability distribution, allowing it to be multi-modal (a person can maintain multiple different identities), uncertain (a person can be unsure about who they really are), and learnable (agents can learn the identities and selves of other agents). We show how the Bayesian affect control theory of self can underpin artificial agents that are socially intelligent. KW - Künstliche Intelligenz KW - Identität KW - Reasoning KW - Wahrscheinlichkeit KW - Affekt KW - Selbstkontrolle Y1 - 2015 SN - 978-1-57735-699-8 SP - 529 EP - 536 PB - AAAI Press CY - Palo Alto, Calif. ER - TY - JOUR A1 - Hoey, Jesse A1 - Schröder, Tobias A1 - Morgan, Jonathan Howard A1 - Rogers, Kimberly B. A1 - Rishi, Deepak A1 - Nagappan, Meiyappan T1 - Artificial Intelligence and Social Simulation BT - Studying Group Dynamics on a Massive Scale JF - Small Group Research N2 - Recent advances in artificial intelligence and computer science can be used by social scientists in their study of groups and teams. Here, we explain how developments in machine learning and simulations with artificially intelligent agents can help group and team scholars to overcome two major problems they face when studying group dynamics. First, because empirical research on groups relies on manual coding, it is hard to study groups in large numbers (the scaling problem). Second, conventional statistical methods in behavioral science often fail to capture the nonlinear interaction dynamics occurring in small groups (the dynamics problem). Machine learning helps to address the scaling problem, as massive computing power can be harnessed to multiply manual codings of group interactions. Computer simulations with artificially intelligent agents help to address the dynamics problem by implementing social psychological theory in data-generating algorithms that allow for sophisticated statements and tests of theory. We describe an ongoing research project aimed at computational analysis of virtual software development teams. KW - Künstliche Intelligenz KW - Gruppendynamik KW - Maschinelles Lernen KW - Virtuelles Team KW - Simulation Y1 - 2018 U6 - https://doi.org/10.1177/1046496418802362 SN - 1552-8278 VL - 49 IS - 6 SP - 647 EP - 683 PB - Sage Publications CY - London ER - TY - CHAP A1 - Morat, Paul A1 - Schwerdtfeger, Aaron A1 - Feucht, Max Emanuel A1 - Heidmann, Frank ED - Yamashta, Naomi ED - Evers, Vanessa ED - Yatani, Koji ED - Ding, Xianghua T1 - Der Trog BT - Prototype for an embodied contactless Breath-To-Water-Interface T2 - CHI EA '25 : Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems N2 - Der Trog is a prototype for a contactless, embodied breath-to-water interface, exploring the potential of Artificial Intelligence as a mediator to facilitate emotional and sensory interactions with nature. By tracking respiration via thermal image analysis and translating it into wave patterns on a water surface, the installation provides a tangible experience translating the user’s corporal rhythm onto a natural material. The present research project investigates whether such interactions can help bridging the gap between environmental knowledge and action within the ecological crisis, encouraging holistic empathy for nature. Der Trog aims to inspire new perceptions of our connection to natural elements, advocating for sustainable behaviour through meaningful, embodied encounters with the environment. KW - Künstliche Intelligenz KW - Maschinelles Sehen KW - Nachhaltigkeit Y1 - 2025 SN - 979-8-4007-1395-8 U6 - https://doi.org/10.1145/3706599.3721173 SP - 1 EP - 5 CY - New York ER - TY - CHAP A1 - Michel, Antje A1 - Griesbaum, Joachim ED - Skov, Mette ED - Innocenti, Perla ED - Late, Elina ED - Gorichanaz, Tim ED - Greifeneder, Elke T1 - AI tool usage by students: tool selection, use cases, quality of results BT - What constitutes AI information literacy and how to support its development? T2 - ISIC 2024 Adjunct Proceedings : The 15th Information Behaviour Conference N2 - The aim of the workshop is to jointly reflect on theses and initial findings on information behaviour of information science students in the context of their use of AI tools and to draw conclusions from them for the further development of the teaching of information literacy. The workshop hosts suggest viewing the event as a kick-off for further collaboration in this topic area. The workshop connects insights from an empirical study conducted by the workshop hosts with theoretical considerations of information literacy and AI literacy. The study offers insights into the use of AI tools by information science students as well as the use cases for the use of AI tools. It also deals with the criteria that are relevant for the use of AI tools as well as the assessment criteria that students apply to the tools and their results. Building on that, the relation of AI information literacy on existing information literacy and AI literacy models will be discussed within the workshop to develop a first approach on the question, what constitutes AI information literacy. The workshop aims to lay a foundation for an ongoing discussion on the question what constitutes AI information literacy and how to foster it. KW - Informationskompetenz KW - Künstliche Intelligenz KW - Lehre KW - Studium Y1 - 2024 SN - 978-87-94563-82-6 U6 - https://doi.org/10.54337/aau738071833 SP - 33 EP - 36 PB - Aalborg University Open Publishing CY - Aalborg ER - TY - JOUR A1 - Rodighiero, Dario A1 - Derry, Lins A1 - Duhaime, Douglas A1 - Kruguer, Jordan A1 - Mueller, Maximilian C. A1 - Pietsch, Christopher A1 - Schnapp, Jeffrey T. A1 - Steward, Jeff T1 - Surprise machines : revealing Harvard Art Museums’ image collection JF - Information Design Journal N2 - Surprise Machines is a project of experimental museology that sets out to visualize the entire image collection of the Harvard Art Museums, with a view to opening up unexpected vistas on more than 200,000 objects usually inaccessible to visitors. The project is part of the exhibition organized by metaLAB (at) Harvard entitled Curatorial A(i)gents and explores the limits of artificial intelligence to display a large set of images and create surprise among visitors. To achieve this feeling of surprise, a choreographic interface was designed to connect the audience’s movement with several unique views of the collection. KW - Künstliche Intelligenz KW - Digitales Archiv KW - Museumskunde KW - Netzwerk KW - Visualisierung Y1 - 2022 U6 - https://doi.org/10.1075/idj.22013.rod SN - 1569-979X VL - 27 IS - 1 SP - 21 EP - 34 PB - John Benjamins Publishing Company CY - Amsterdam ER - TY - BOOK ED - Dreisiebner, Stefan ED - Griesbaum, Joachim Bertold ED - Michel, Antje ED - Tappenbeck, Inka ED - Wittich, Anke T1 - Informationskompetenz und Künstliche Intelligenz BT - Konzepte, Herausforderungen und Chancen. Ein Beitrag der Fachgruppe Informationskompetenz der KIBA N2 - Am 20. September 2024 organisierte die Fachgruppe Informationswissenschaft der KIBA an der Universität Hildesheim einen Workshop zum Thema „Informationskompetenz und Künstliche Intelligenz“. Ziel des Workshops war es, das Konzept der Informationskompetenz im Kontext der neuen Herausforderungen und Chancen, die mit der rasanten Verbreitung von Künstlicher Intelligenz (insbesondere generativer Künstlicher Intelligenz seit Ende 2022) einhergehen, aus einer forschungsbezogenen Perspektive zu beleuchten und zu diskutieren. 13 Expert:innen, die zuvor ein Positionspapier zu diesem Thema verfasst hatten, nahmen am Workshop teil. Die Positionspapiere und die Workshop-Diskussion konzentrierten sich auf drei zentrale Fragestellungen: 1. Welche Auswirkungen hat Künstliche Intelligenz auf bestehende Konzepte der Informationskompetenz? 2. Welche neuen Fragestellungen ergeben sich aus Künstlicher Intelligenz für informationswissenschaftliche Forschung im Bereich Informationskompetenz? 3. Welche Herausforderungen und Chancen ergeben sich in der Förderung von Informationskompetenz durch Künstliche Intelligenz? Dieser Band präsentiert die Positionspapiere sowie die zentralen Ergebnisse des Workshops. KW - Informationskompetenz KW - Künstliche Intelligenz KW - Didaktik Y1 - 2024 U6 - https://doi.org/10.18442/318 PB - Universitätsverlag Hildesheim CY - Hildesheim ER - TY - JOUR A1 - Safariallahkheili, Qasem A1 - Schiewe, Jochen A1 - Meier, Sebastian T1 - Post-Hoc Explanation of AI Predictions in Wildfire Risk Mapping Through an Interactive Web-Based GeoXAI System JF - KN - Journal of cartography and geographic information N2 - This case study presents an innovative approach for explaining wildfire susceptibility through a web-based Geospatial eXplainable Artificial Intelligence (GeoXAI) system. By addressing limitations in traditional GeoXAI tools, such as the lack of geographical context for model predictions and local explanation, this system integrates state-of-the-art XAI methods with open-source geospatial technologies. Applied to the wildfire-prone regions of Berlin and Brandenburg, Germany, the system combines environmental, topographic, and meteorological features derived from high-resolution geospatial data for training a Random Forest (RF) model. The web-based GeoXAI system enables interactive exploration of the model output and its features, allowing users to visualize wildfire susceptibility, examine feature contributions, and correlate predictions with spatial patterns through post-hoc interpretability. By employing post-hoc explanation methods like SHAP, the system offers clear insights into model predictions by analyzing feature contributions after training, which helps users better understand AI-driven outcomes. Designed with a user-centered approach, the platform promotes trust and usability through transparent predictions, interactive geovisualizations, and local explanations, allowing users to navigate spatial data intuitively by exploring overviews, focusing on specific regions, and accessing detailed insights on demand. This work highlights the potential of combining GeoXAI with machine learning to improve decision-making in wildfire prevention and management. KW - Entscheidungsunterstützungssystem KW - Fernerkundung KW - Künstliche Intelligenz KW - Waldbrand KW - GeoXAI Y1 - 2025 U6 - https://doi.org/10.1007/s42489-025-00194-0 SN - 2524-4957 SN - 2524-4965 PB - Springer International Publishing CY - Cham ER - TY - RPRT ED - Dreisiebner, Stefan ED - Gäde, Maria ED - Griesbaum, Joachim ED - Michel, Antje ED - Petschenka, Anke ED - Tappenbeck, Inka ED - Wittich, Anke T1 - Fachspezifische Informationskompetenz-Vermittlung und Künstliche Intelligenz BT - Ein Beitrag der Fachgruppe Informationskompetenz der KIBA N2 - Am 24.10.2025 veranstaltete die Arbeitsgruppe Informationskompetenz der Konferenz der informations- und bibliothekswissenschaftlichen Ausbildungs- und Studiengänge (KIBA) einen Workshop zum Thema „Fachspezifische Informationskompetenz-Vermittlung und Künstliche Intelligenz“ an der Technischen Informationsbibliothek Hannover. Ziel der Veranstaltung war es, fachspezifische Perspektiven auf den Einsatz und die Vermittlung von Künstlicher Intelligenz (KI) im Kontext der Informationskompetenz zusammenzuführen und kritisch zu reflektieren. Eingeladen waren Expert:innen aus Forschung und Praxis der Bibliotheks- und Informationswissenschaft. Die Teilnehmenden reichten im Vorfeld Experience Papers ein, in denen sie ihre Erfahrungen, Konzepte und Fragestellungen darlegten. Grundlage bildeten drei zentrale Leitfragen: (1) fachspezifische Besonderheiten bei der Anwendung von KI in Informationskompetenz-Schulungen, (2) didaktische Konzepte und Vermittlungsansätze zur Förderung von Kenntnissen und praktischen Fähigkeiten im KI-Kontext sowie deren institutionelle Einbettung, und (3) Bezüge zu bestehenden Kompetenzrahmen wie dem AI Literacy Framework der Europäischen Kommission oder dem ACRL-Framework für Informationskompetenz sowie deren Grenzen. Der Workshopband bündelt die Beiträge entlang dieser Fragestellungen, fasst die Diskussionen und zentralen Ergebnisse des Treffens zusammen und dokumentiert weiterführende Perspektiven in Form von MindMaps zu den Positionspapieren. Insgesamt leistet der Band einen Beitrag zur Weiterentwicklung der fachspezifischen Informationskompetenz-Vermittlung im Spannungsfeld von Künstlicher Intelligenz, didaktischen Anforderungen und institutionellen Rahmenbedingungen. KW - Bibliothekswissenschaft KW - Didaktik KW - Informations- und Dokumentationswissenschaft KW - Informationskompetenz KW - Künstliche Intelligenz Y1 - 2026 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:11-110-18452/36579-2 CY - Berlin ER - TY - CHAP A1 - Griesbaum, Joachim A1 - Dreisiebner, Stefan A1 - Michel, Antje A1 - Tappenbeck, Inka A1 - Wittich, Anke ED - Kurbanoğlu, Serap ED - Boustany, Joumana ED - Špiranec, Sonja ED - Ünal, Yurdagül ED - Şencan, İpek ED - Kos, Denis ED - Grassian, Esther ED - Roy, Loriene ED - Mizrachi, Diane T1 - Information Literacy and Artificial Intelligence BT - A Library and Information Science Perspective on Effects, Research Questions, Challenges and Opportunities T2 - Information Literacy in an AI-Driven World : 9th European Conference, ECIL 2025, Bamberg, Germany, September 22–25, 2025, Revised Selected Papers N2 - This article summarizes the results of a workshop at which thirteen librarians and information scientists discussed the following topics: 1. The impact of Artificial Intelligence (AI) on information literacy. 2. Questions arising from AI for research on information literacy. 3. Challenges and opportunities for the promotion of information literacy through AI. Results indicate: 1. Generative AI is transforming information markets by reshaping how information is produced and used. New search services emerge, creating new opportunities for the provision and use of information, but also challenging established competencies for assessing quality and transparency. 2. Key research questions include: How does AI reshape user behavior? How can content quality and transparency be ensured? Can labelling and regulation work? How can AI skills and lifelong learning be advanced? 3. AI offers benefits like automating tasks and supporting learning but also raises concerns about transparency and autonomy. Information literacy requires interdisciplinary education and a critical evaluation of AI tools. KW - Benutzerverhalten KW - Bibliothekswissenschaft KW - Informations- und Dokumentationswissenschaft KW - Informationskompetenz KW - Künstliche Intelligenz Y1 - 2026 SN - 978-3-032-17272-3 U6 - https://doi.org/10.1007/978-3-032-17272-3_1 SN - 1865-0937 SP - 3 EP - 13 PB - Springer International Publishing CY - Cham ER - TY - JOUR A1 - Cunitz, Carla A1 - Ortmanns, Meike A1 - Schröder, Tobias A1 - Hantzsche, Michael A1 - Schiewe, André A1 - Nachtwei, Jens T1 - Wenn KI mitarbeitet JF - Innovative Verwaltung : Das Fachmedium für erfolgreiches Verwaltungsmanagement N2 - In einem Forschungsprojekt in Berlin und Potsdam wurde untersucht, wie Mitarbeitende Künstliche Intelligenz (KI) in ihrer täglichen Arbeit wahrnehmen und welche Erwartungen sie an smarte Tools haben. Die Studie zeigt, dass 58 Prozent der Beschäftigten KI positiv sehen und sich vor allem Entlastung bei Routineaufgaben erhoffen. Besonders beliebt sind KI-Tools wie ChatGPT, Microsoft Copilot und Perplexity, die bei Texterstellung, Recherche und Datenanalyse unterstützen. Die Ergebnisse der Studie fließen in ein Kooperationsprojekt zur Gestaltung zukunftsfähiger Arbeitswelten ein. In Workshops wurden konkrete KI-Nutzungsszenarien für die Verwaltungspraxis erarbeitet, darunter die Durchsuchbarkeit von Dienstvorschriften, KI-basierte Texterstellung und ein intelligenter Bürgerservice-Chatbot. Die Einführung von KI erfordert Transparenz, Weiterbildung, konkrete Anwendungsfelder und Partizipation der Mitarbeitenden. Durch positive Erfahrungen mit KI-Anwendungen kann die Akzeptanz für komplexere Technologien gesteigert werden. Die Studie bietet eine Blaupause für andere Kommunen und zeigt, wie KI gezielt und mitarbeitendenorientiert eingesetzt werden kann, um greifbaren Mehrwert für die Verwaltungsarbeit zu schaffen. KW - Arbeit KW - Generative KI KW - Künstliche Intelligenz Y1 - 2026 UR - https://www.springerprofessional.de/wenn-ki-mitarbeitet/52027876?searchResult=1.wenn%20ki%20mitarbeitet SN - 1618-9876 VL - 2026 IS - 1-2 SP - 34 EP - 37 PB - Springer Gabler CY - Wiesbaden ER -