@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{12689, author = {{Latos, Benedikt and Becks, Daniela and Gaillard, Antoine and Perau, Martin and Respondek, Bernd and Kranz, Michael and Kukulies, Jan and Kruse, Christian }}, booktitle = {{Twentieth International Conference on Environmental, Cultural, Economic & Social Sustainability Conference Proceedings}}, editor = {{Humphreys, David }}, isbn = {{978-1-963049-41-1}}, keywords = {{ESG, AI, SME}}, location = {{Aveiro, Portugal}}, publisher = {{University of Aveiro}}, title = {{{Integration of Generative AI into a tool to assist participatory ESG double materiality assessment for SMEs}}}, year = {{2024}}, } @inbook{13169, abstract = {{KI.BAU is a project being developed and conducted at the Detmold School of Design, part of the University of Applied Sciences and Arts Ostwestfalen-Lippe. It focuses on researching the application of artificial intelligence (AI) in architectural design, modelling, production and management processes, particularly on the communication between users, processes and the building itself in various development and life-time phases. Hence the research aims to develop new tools and AI-supported process chains for the design, production and communication of architecture. This includes the training and implementing prototypical machine learning algorithms to autonomously evolve and optimize field-specific processes and workflows. As mentioned above, a critical question KI.BAU explores is how we, as planners, builders and users, will communicate with architecture in the future, in its phases of creation and use but also beyond. This also involves, besides virtual interfaces, examining the physical interaction with a building, its behaviour, responsiveness and adaptation to certain conditions. The primary goal of the research at KI.BAU is to transform architecture into an intelligent, to some degree self-sustaining, self-reflective and maybe even evolving ‘ecological system’. This system should be comprehensively linked with its creators, users, devices, computers, its (biological) environment and networks. Consequently, a building must be viewed as an organism that communicates, interacts and adapts to other connected or related organisms and entities. }}, author = {{Sachs, Hans}}, booktitle = {{Synthetic realities: New Frontiers in AI-driven Design, Fabrication and Materiality}}, editor = {{Kretzer, Manuel}}, isbn = {{978-3887781088}}, keywords = {{AI, Artificial Intelligence, Architecture, Build Environment, Building Construction, Ecology of Architecture}}, pages = {{14}}, publisher = {{AADR – Art Architecture Design Research}}, title = {{{KI.BAU Artificial Intelligence in Architecture}}}, year = {{2024}}, } @misc{10935, abstract = {{One of the challenges in universities is to take into account the different competences of students throughout the process of imparting knowledge. It is unlikely that all students will grasp the course content in the same way, as they have different abilities. Similarly, it is unrealistic for teachers to address all the individual needs of each student at all times. In addition, maintaining learners' attention to a particular topic is also an essential part of teaching. To overcome these challenges, we are developing AI learning software as part of a research project. The software generates learning scenarios for the students and takes their individual competences into account. The research project focuses on the field of automation technology, especially on programming, as programming skills are usually required in automation environments. When solving the scenarios, students receive immediate feedback on how well they have solved the task. Immediate automated feedback not only meets the students' expectations of quick learning feedback, but it also relieves the teaching staff in the assessment process. In addition, the software includes an interactive user interface that allows learners to see the results of their programming tasks in a simulated 3D environment. This approach aims to maintain learners' attention over a longer learning period. This paper describes the above aspects that are being developed in the research project and previews the new learning opportunities that could arise from the use of AI.}}, author = {{Ali, Asmar and Deuter, Andreas}}, booktitle = {{Journal of international scientific publications / Science & Education Foundation : Educational Alternatives }}, issn = {{1313-2571}}, keywords = {{ai education, automated feedback, automation education, educational software, programming}}, location = {{Burgas}}, pages = {{12--20}}, publisher = {{Info Invest }}, title = {{{An AI Assistant for Education in Automation}}}, doi = {{10.62991/EA1996108906}}, volume = {{21}}, year = {{2023}}, } @misc{12994, abstract = {{HUMAN 2023 is the 6th workshop of a series for the ACM Hypertext conferences. The HUMAN workshop has a strong focus on the user and thus is complementary to the strong machine analytics research direction that could be experienced in previous conferences.The user-centric view on hypertext not only includes user interfaces and interaction, but also discussions about hypertext application domains as well as human-centered AI. Furthermore, the workshop raises the question of how original hypertext ideas (e. g., Doug Engelbart’s "augmenting human intellect" [7] or Jeff Conklin’s "hypertext as a computer-based medium for thinking and communication" [6]) can improve today’s hypertext systems.}}, author = {{Rubart, Jessica and Atzenbeck, Claus}}, booktitle = {{Proceedings of the 34th ACM Conference on Hypertext and Social Media}}, isbn = {{979-8-4007-0232-7}}, keywords = {{user interfaces, information structuring, decision making, human-centered AI, cognitive aspects, scientific community, digital humanities, user interaction, human factors, user-centric, annotation, adaptive hypertext, hypermedia, collaboration, information systems, augmentation, hypertext, communication, intercultural aspects}}, location = {{Rome, Italy}}, publisher = {{ACM}}, title = {{{HUMAN’23: 6th Workshop on Human Factors in Hypertext}}}, doi = {{10.1145/3603163.3610576}}, year = {{2023}}, } @misc{13020, abstract = {{Developing AI systems for automatic train operation (ATO) requires developers to have a deep understanding of the human tasks they are trying to replace. This paper fills this gap and translates the regulatory requirements from the context of German railways for the AI developer community. As a result, tasks such as train’s path monitoring for collision prediction, signal detection, door operation, etc. are identified. Based on this analysis, a functionally justified sensor setup with detailed configuration requirements is presented. This setup was also evaluated by a survey within the railway industry. The evaluated sensors include RGB/IR cameras, LIDARs, radars and ultrasonic sensors. Calculations and estimates for the evaluated sensors are presented graphically and included in this paper. However, the ultimate sensor setup is still a subject of research. The results of this paper also address the lack of training and test datasets for railway AI systems. It is proposed to acquire research datasets that will allow the training of domain adaptation algorithms to transform other datasets, thus increasing the number of available datasets. The sensor setup is also recommended for such research datasets.}}, author = {{Tagiew, Rustam and Leinhos, Dirk and von der Haar, Henrik and Klotz, Christian and Sprute, Dennis and Ziehn, Jens and Schmelter, Andreas and Witte, Stefan and Klasek, Pavel}}, booktitle = {{Discover Artificial Intelligence}}, issn = {{2731-0809}}, keywords = {{Automatic train operation, ATO, GoA3, GoA4, Perception, AI}}, number = {{1}}, publisher = {{Springer International Publishing }}, title = {{{Sensor system for development of perception systems for ATO}}}, doi = {{10.1007/s44163-023-00066-4}}, volume = {{3}}, year = {{2023}}, } @article{10645, abstract = {{Artificial Intelligence (AI)-based applications promise great potential benefits for companies. However, an isolated consideration of the technical system is not sufficient for the design. Rather, it is necessary to design the entire work system taking into account the socio-technical system approach. This enables the combination of the strengths of people and intelligent systems. This paper presents an approach for a socio-technical requirements elicitation in the design of AI-based systems by adapting the HTO-analysis. First, a mission statement is developed. Based on a detailed process modelling, existing data and systems are recorded. In addition, all relevant stakeholder groups are included by conducting interviews and surveys. Thus, the procedure enables the derivation of a comprehensive catalogue of requirements. The application of the approach is illustrated by using an example from industrial practice, the design of an intelligent workforce planning system.}}, author = {{Gabriel, Stefan and Bentler, Dominik and Grote, Eva-Maria and Junker, Caroline and Wendischhoff, David Meyer zu and Bansmann, Michael and Latos, Benedikt and Hobscheidt, Daniela and Kühn, Arno and Dumitrescu, Roman}}, issn = {{2212-8271}}, journal = {{Procedia CIRP}}, keywords = {{socio-technial design socio-technial design, requirements elicitation, AI-human-collaboration, work design : workforce planning requirements elicitation AI-human-collaboration work design workforce planning}}, pages = {{431--436}}, publisher = {{Elsevier BV}}, title = {{{Requirements analysis for an intelligent workforce planning system: a socio-technical approach to design AI-based systems}}}, doi = {{10.1016/j.procir.2022.05.274}}, volume = {{109}}, year = {{2022}}, } @inbook{6910, abstract = {{AI is on the rise. Powerful cloud platforms and networked software components can perform increasingly complex data evaluations and simulations. Recent research and development projects1 show how great the potential of artificial intelligence is for urban planning. However, despite the impressive, technical possibilities, it currently remains unclear how planning stakeholders and the affected population can be meaningfully involved in the intelligent processes of the "black box". The authors are of the opinion that sustainable urban development planning not only requires acceptance of the spatial planning result, as has been the case up to now, but also requires acceptance of the increasingly digitally supported planning process. For this reason, it must also be possible for laypersons to understand the digital analysis and evaluation processes and to comprehend their relevance and spatial interactions. Consequently, simulations must not only run in the computers of the respective planning or engineering offices, but require a simple, haptic analog translation that can also be used in participation processes as already shown in the CityScope projects2. For this project, the big revitalization project of Deutzer Hafen in Cologne to a future district with more than 9.500 daily users is used as a case study in building a decision support system for urban planning. It is composed of three parts: an agent-based model, a tangible user interface and a synthetic population. The project enables users to get in touch with an agent-based model (ABM) without any knowledge in coding or even interacting with computers. It connects physical objects to digital information. Based on the theories of Castiglione et.al.3 , Gehl4 , Shannon5 and Jacobs6 this project shows how to use an artificial and analog simulation model to measure the urban vitality of the public spaces in the district, based on the activity and travelling patterns of the population. This is done by testing different scenarios in which we change interactive parameters of the model: the use of the buildings and the demographics of the population. We can then determine which scenarios benefit the most life in the public spaces of the district, by finding areas of interest or problematic ones. }}, author = {{Barbosa Jardim, Amanda and Müh, Maximilian and Häusler, Axel and Kondziela, Andrea}}, booktitle = {{ REAL CORP 2021: Cities 20.50, creating habitats for the 3rd millennium, smart - sustainable - climate neutral : proceedings of 26th International Conference on Urban Planning, Regional Development and Information Society}}, editor = {{Schrenk, Manfred and Popovich, Vasily V. and Zeile, Peter and Elisei, Pietro and Beyer, Clemens and Ryser, Judith and Stöglehner, Gernot}}, isbn = {{978-3-9504945-0-1}}, keywords = {{mart Cities, Agent-based modelling, KI/AI, Participation, Tangible Data}}, location = {{Wien}}, publisher = {{CORP - Competence Center of Urban and Regional Planning}}, title = {{{Synthetic and Tangible Agents for an Activity-based Urban Planning Tool}}}, doi = {{10.48494/REALCORP2021.1049}}, year = {{2021}}, }