@misc{13529, abstract = {{The proliferation of misinformation is one of the most pressing challenges in today’s digital landscape, due to its far-reaching implications for public health, economic stability, trust in governmental institutions, and societal cohesion. Despite efforts to regulate online platforms and limit the spread of misinformation, many individuals are left behind because of their low digital literacy, level of education, and other contributing factors. In this context, we explore the use of Large Language Models (LLMs) to identify misinformation and we evaluate the capabilities of GPT-4.1-mini, as a representative example of these models. We then discuss how LLMs can help empower users to critically create and share information, thereby fostering more resilient online communities. We also present a set of possible interaction patterns for content creation and moderation.}}, author = {{Franco, Mirko and Grimm, Valentin and Herder, Eelco}}, booktitle = {{Proceedings of the 2025 International Conference on Information Technology for Social Good}}, editor = {{Marquez-Barja, Johann and Bujari, Armir and Slamnik-Kriještorac, Nina and Sabbioni, Andrea}}, isbn = {{979-8-4007-2089-5}}, keywords = {{misinformation, fake news, large language models, online social networks}}, location = {{Antwerp, Belgium}}, pages = {{244 -- 252}}, publisher = {{ACM}}, title = {{{Preventing Accidental Sharing of Misinformation Using Large Language Models}}}, doi = {{10.1145/3748699.3749798}}, year = {{2025}}, } @misc{12048, abstract = {{Interactive stories can be an effective approach for teaching purposes. One shortcoming is the effort necessary to author and create these stories, especially complex storylines with choices for the readers. Based on recent advances in Natural Language Processing (NLP), new opportunities arise for assistance systems in the context of interactive stories. In our work, we present an authoring approach and prototypical tool for the creation of visual comic-strip like interactive stories, a type of hypercomics, that integrate an Artificial Intelligence (AI) assistance. Such comics are already used in our Gekonnt hanDeln web platform. The AI assistance provides suggestions for the overall story outline as well as how to design and write individual story frames. We provide a detailed description about the approach and its prototypical implementation. Furthermore, we present a study evaluating the prototype with student groups and how the prototype evolved in an iterative style based on the students’ feedback.}}, author = {{Grimm, Valentin and Rubart, Jessica}}, booktitle = {{HT '24: Proceedings of the 35th ACM Conference on Hypertext and Social Media}}, isbn = {{979-8-4007-0595-3 }}, keywords = {{Storytelling, Authoring, GPT, Hypercomics, Large Language Models}}, location = {{Poznan, Poland}}, pages = {{88--97}}, publisher = {{Association for Computing Machinery (ACM)}}, title = {{{Authoring Educational Hypercomics assisted by Large Language Models}}}, doi = {{10.1145/3648188.3675124}}, year = {{2024}}, } @misc{12383, abstract = {{Data stories are about revealing and communicating insights from complex data. In this paper, we propose conversational data stories, which support end users in understanding the key findings of the data analysis at hand by natural language conversation. Creating these stories manually means to put a lot of effort into understanding the data and crafting visuals. With increasingly powerful generative large language models (LLMs), natural language processing as well as automating the creation of data stories is a promising field. We present a concept for a conversational data storytelling system that integrates LLMs as well as explainable AI. We present the collected requirements for our system concept and how the requirements are addressed. To show the potential of our approach, we provide a use case scenario and a discussion in this paper. This is supposed to serve as a basis for future research that will aim at investigating the technical reliability and the user experience of such a system.}}, author = {{Grimm, Valentin and Rubart, Jessica and Söhlke, Patrick}}, booktitle = {{Proceedings of the 7th Workshop on Human Factors in Hypertext (HUMAN'24)}}, editor = {{Atzenbeck, Claus and Rubart, Jessica}}, isbn = {{979-8-4007-1120-6}}, keywords = {{Data Storytelling, Conversational Assistant, Conversational Data Storytelling, Explainable AI}}, location = {{Poznan Poland}}, pages = {{6}}, publisher = {{ACM}}, title = {{{Conversational Data Stories}}}, doi = {{10.1145/3679058.3688631}}, year = {{2024}}, } @misc{10309, author = {{Grimm, Valentin and Rubart, Jessica and Faller, Gudrun and Geiger, Laura}}, booktitle = {{Mensch und Computer 2023 – Workshopband, MCI-WS02: Partizipative und sozialverantwortliche Technikentwicklung}}, editor = {{Fröhlich, Peter and Cobus, Vanessa}}, location = {{Rapperswil, Schweiz}}, publisher = {{Gesellschaft für Informatik e.V.}}, title = {{{Partizipativer Entwicklungsprozess einer Trainingsplattform für Haushaltshilfen}}}, doi = {{10.18420/muc2023-mci-ws02-258}}, year = {{2023}}, } @misc{10311, author = {{Grimm, Valentin and Rubart, Jessica}}, booktitle = {{Mensch und Computer 2023 – Workshopband, MCI-WS08 6th International Workshop Gam-R – Gamification Reloaded}}, editor = {{Fröhlich, Peter and Cobus, Vanessa}}, location = {{Rapperswil, Schweiz}}, publisher = {{Gesellschaft für Informatik e.V.}}, title = {{{Unlocking E-learning and XAI Concepts with Free Limited Choice}}}, doi = {{10.18420/muc2023-mci-ws08-354}}, year = {{2023}}, } @misc{10312, author = {{Potthast, Jonas and Grimm, Valentin and Rubart, Jessica}}, booktitle = {{Mensch und Computer 2023 – Workshopband, MCI-WS16 - UCAI 2023: Workshop on User-Centered Artificial Intelligence}}, editor = {{Fröhlich, Peter and Cobus, Vanessa}}, location = {{Rapperswil, Schweiz}}, publisher = {{Gesellschaft für Informatik e.V.}}, title = {{{Immersive Exploration of Machine Learning Data Combining Visual Analytics with Explainable AI}}}, doi = {{10.18420/muc2023-mci-ws16-389}}, year = {{2023}}, } @misc{10789, abstract = {{Explainable AI (XAI) provides approaches and techniques for building trust in AI models. This paper presents and explores XAI approaches focusing on user interface concepts in predictive maintenance. The underlying AI model is based on an open dataset for wind turbines. An enhanced multi-class self-conceived labeling strategy improves the model and, thus, supports the XAI approaches. Previous research in user-centered XAI shows that users do not exploit the possibilities of XAI methods and instead rely on their intuition. To counter this tendency, we present user interfaces incorporating gamification elements to enhance understanding of AI outputs. We highlight our approach via two examples, demonstrating a local and a global XAI technique respectively. A preliminary user study was conducted to assess the value added by these gamification aspects. While the findings were inconclusive, they provided an initial insight into the potential of these design elements to foster user engagement in the realm of XAI.}}, author = {{Grimm, Valentin and Potthast, Jonas and Rubart, Jessica}}, booktitle = {{2023 IEEE 21st International Conference on Industrial Informatics (INDIN)}}, keywords = {{XAI, Industrial Analytics, Motivational Exploration, SHAP}}, location = {{Lemgo}}, pages = {{1--6}}, publisher = {{IEEE}}, title = {{{Motivational Exploration of Explanations in Industrial Analytics}}}, doi = {{10.1109/INDIN51400.2023.10217864}}, year = {{2023}}, } @misc{11758, abstract = {{This demo features a prototype of a software tool to guide developers through the process of gamification design. It shall improve gameful designs, especially in the scientific community, and make the process of gamification design more accessible for non-experts in the field. The main goal of this first version is to gain user feedback on its usefulness with respect to intuition of use, its documentation capabilities and collaboration.}}, author = {{Grimm, Valentin}}, booktitle = {{Proceedings of Mensch und Computer 2022 }}, editor = {{Mühlhäuser, Max }}, location = {{Darmstadt}}, pages = {{605--607}}, publisher = {{ACM}}, title = {{{A Gamification Design Tool to Improve Design Quality}}}, doi = {{10.1145/3543758.3547510}}, year = {{2022}}, } @misc{9159, abstract = {{Beschäftigte in haushaltsnahen Dienstleistungen wurden bislang gesellschaftlich wenig beachtet und wissenschaftlich kaum untersucht. Der Beitrag zeigt, weshalb und wie im Rahmen eines Forschungsprojektes der Fokus auf diese Gruppe gelegt wird, von welchen Rahmenbedingungen die Beschäftigung in haushaltsnahen Dienstleistungen gekennzeichnet ist und welche Erkenntnisse im Projekt bereits zu Arbeitsbedingungen, Belastungen und Ressourcen der Beschäftigten gewonnen werden konnten.}}, author = {{Geiger, Laura and Faller, Gudrun and Grimm, Valentin and Rubart, Jessica and Weber, Martin and Vennebusch, Thorsten}}, booktitle = {{ Betriebliche Prävention : Arbeit, Gesundheit, Unfallversicherung}}, issn = {{2365-7634}}, number = {{9}}, pages = {{376--379}}, publisher = {{Erich Schmidt Verlag}}, title = {{{Gekonnt handeln – Prävention für Beschäftigte in haushaltsnahen Dienstleistungen}}}, doi = {{https://doi.org/10.37307/j.2365-7634.2022.09.08}}, volume = {{134}}, year = {{2022}}, } @misc{9161, abstract = {{Employees in household-related services have so far been neglected in research and practice. The overall goal of our project is to identify work-related stress of this special target group, develop recommendations, and disseminate them using low-threshold, attractive edutainment offers. In this context, this contribution presents a learning platform design for the special target group of domestic workers, such as gardeners or cleaners. The design is based on a requirements analysis with respect to this special target group, which we as well outline in this contribution.}}, author = {{Grimm, Valentin and Geiger, Laura and Rubart, Jessica and Faller, Gudrun}}, booktitle = {{DELFI 2022 : die 20. Fachtagung Bildungstechnologien der Gesellschaft für Informatik e.V., 12.-14. September 2022, Karlsruhe}}, editor = {{Henning, Peter A. and Striewe, Michael and Wölfel, Matthias}}, isbn = {{978-3-88579-716-6}}, issn = {{1617-5468}}, keywords = {{E-Learning, Minority Group, Gameful Design, Gamification}}, location = {{Karlsruhe, DE}}, pages = {{213--214}}, publisher = {{Gesellschaft für Informatik e.V.}}, title = {{{Requirements and Design of a Training System for Domestic Workers}}}, doi = {{10.18420/delfi2022-037}}, volume = {{P-322}}, year = {{2022}}, } @misc{9162, abstract = {{The German manufacturing industry has been carrying out new developments towards the next industrial revolution, focusing on smart manufacturing environments. Our work emphasizes human-centered control rooms in the context of production plants. Increased automation does not have to come with less human control. Therefore, we report on multimodal collaborative interaction techniques to augment industrial control rooms. In particular, we include mobile workers who use the control room while being in the production hall using tablets or specifically mixed reality glasses. Collaborative annotation dashboards support discussions and a shared understanding among analysts. Manufacturing-related data can be integrated into business analytics environments so that holistic analyses can be performed. Multimodal interaction techniques can support effective interaction with the control room based on the users’ preferences. Immersive experience through mixed reality-based three-dimensional visualizations and interaction possibilities support users in obtaining a clear understanding of the underlying data.}}, author = {{Rubart, Jessica and Grimm, Valentin and Potthast, Jonas}}, booktitle = {{Journal Future Internet}}, issn = {{1999-5903 }}, keywords = {{control room, multimodel interaction, augmented reality, mixed reality}}, number = {{8}}, pages = {{1--18}}, publisher = {{MDPI}}, title = {{{Augmenting Industrial Control Rooms with Multimodal Collaborative Interaction Techniques}}}, doi = {{https://doi.org/10.3390/fi14080224}}, volume = {{14}}, year = {{2022}}, } @misc{9163, author = {{Potthast, Jonas and Grimm, Valentin and Rubart, Jessica}}, booktitle = {{Proceedings of Mensch und Computer 2022}}, editor = {{Mühlhäuser, Max}}, location = {{Darmstadt, DE}}, pages = {{594 -- 596}}, publisher = {{ACM Press}}, title = {{{Immersive Experience of Multidimensional Data using Mixed Reality based Scatterplots}}}, doi = {{https://doi.org/10.1145/3543758.3547515}}, year = {{2022}}, } @misc{8326, abstract = {{Gamification is a widely used way of increasing motivation and fun in the use of systems that are not games. By outlining critical aspects in developing gamified systems, we adapted the modelling technique event storming in the context of a special case study where a gamified, collaborative platform was developed. For this purpose, the relationship of event storming and spatial hypertext has been worked out and an event storming extension has been introduced based on spatial hypertext principles. With respect to the case study and further insights in the academic context, we discuss how the emerging nature of event storming could benefit from a specialized spatial hypertext tool.}}, author = {{Grimm, Valentin and Rubart, Jessica}}, booktitle = {{Proceedings of the 4th international Workshop on Human Factors in Hypertext (HUMAN’21)}}, editor = {{Rubart, Jessica and Atzenbeck, Claus}}, isbn = {{978-1-4503-8560-2}}, keywords = {{Spatial Hypertext, Gamification, Event Storming, System Modeling, Collaborative Modeling, Gamified System}}, location = {{Virtual Event, Ireland}}, pages = {{3--10}}, publisher = {{ACM Press}}, title = {{{Modelling Gamified Systems with Event Storming Augmented by Spatial Hypertext}}}, doi = {{10.1145/3468143.3483927}}, year = {{2021}}, }