@inproceedings{61243, author = {{Fisher, Josephine Beryl and Terfloth, Lutz}}, booktitle = {{ Proceedings of the 29th Workshop on the Semantics and Pragmatics of Dialogue (SemDial 2025)}}, title = {{{The Dual Nature as a Local Context to Explore Verbal Behaviour in Game Explanations}}}, year = {{2025}}, } @article{61285, author = {{Herzig, Bardo}}, issn = {{1862-5231}}, journal = {{Erziehungswissenschaft. Mitteilungen der Deutschen Gesellschaft für Erziehungswissenschaft. Erziehungswissenschaft nach ChatGPT}}, number = {{70, Jg.36/2025}}, pages = {{13--18}}, publisher = {{Barbara Budrich}}, title = {{{Die (produktive) Rolle von generativer KI im erziehungswissenschaftlichen Diskurs}}}, year = {{2025}}, } @unpublished{61294, abstract = {{Human-AI collaboration is increasingly promoted to improve high-stakes decision-making, yet its benefits have not been fully realized. Application-grounded evaluations are needed to better evaluate methods for improving collaboration but often require domain experts, making studies costly and limiting their generalizability. Current evaluation methods are constrained by limited public datasets and reliance on proxy tasks. To address these challenges, we propose an application-grounded framework for large-scale, online evaluations of vision-based decision-making tasks. The framework introduces Blockies, a parametric approach for generating datasets of simulated diagnostic tasks, offering control over the traits and biases in the data used to train real-world models. These tasks are designed to be easy to learn but difficult to master, enabling participation by non-experts. The framework also incorporates storytelling and monetary incentives to manipulate perceived task stakes. An initial empirical study demonstrated that the high-stakes condition significantly reduced healthy distrust of AI, despite longer decision-making times. These findings underscore the importance of perceived stakes in fostering healthy distrust and demonstrate the framework's potential for scalable evaluation of high-stakes Human-AI collaboration. }}, author = {{Johnson, David S.}}, title = {{{Higher Stakes, Healthier Trust? An Application-Grounded Approach to Assessing Healthy Trust in High-Stakes Human-AI Collaboration}}}, year = {{2025}}, } @article{61189, author = {{Herzig, Bardo}}, issn = {{1433-4674}}, journal = {{SchulVerwaltung Bayern }}, number = {{7-8}}, pages = {{213--215}}, publisher = {{Carl Link}}, title = {{{Künstliche Intelligenz und professionsbezogene Aufgaben von Lehrkräften}}}, year = {{2025}}, } @inproceedings{60027, author = {{Hellmich, Frank and Hoya, Fabian Karl and Schulze, Jan Roland and Blumberg, Eva}}, publisher = {{Pädagogische Hochschule Vorarlberg}}, title = {{{Team-Teaching angehender Lehrkräfte und Kompetenzen von Kindern im inklusiven naturwissenschaftlichen Sachunterricht. Vortrag auf der Tagung der Kommission „Grundschulforschung und Pädagogik der Primarstufe“, Sektion Schulpädagogik, Deutsche Gesellschaft für Erziehungswissenschaft (DGfE). Thema: „Bildung ohne Grenzen denken. Visionen der Grundschulbildung im europäischen Raum" }}}, year = {{2025}}, } @inproceedings{60028, author = {{Löper, Marwin Felix and Hellmich, Frank}}, publisher = {{Pädagogische Hochschule Vorarlberg}}, title = {{{Einstellungen und prosoziales Verhalten von Grundschulkindern gegenüber Peers mit emotional-sozialem Förderbedarf im inklusiven Unterricht. Vortrag auf der Tagung der Kommission „Grundschulforschung und Pädagogik der Primarstufe“, Sektion Schulpädagogik, Deutsche Gesellschaft für Erziehungswissenschaft (DGfE). Thema: „Bildung ohne Grenzen denken. Visionen der Grundschulbildung im europäischen Raum"}}}, year = {{2025}}, } @inproceedings{59251, author = {{Finke, Pauline and Schulze, Jan Roland and Hellmich, Frank}}, publisher = {{University of Belgrade}}, title = {{{Exploring primary school teachers’ and special education teachers’ self-efficacy beliefs concerning inclusive education: A qualitative study. Paper. ECER 2025 (European Conference on Educational Research). “Charting the Way Forward: Education, Research, Potentials and Perspectives“ }}}, year = {{2025}}, } @inproceedings{59249, author = {{Finke, Nils and Knickenberg, Margarita and Hellmich, Frank}}, publisher = {{BelgradeUniversity of Belgrade}}, title = {{{The role of teacher feedback for primary school students’ motivation and mathematical competencies. Paper. ECER 2025 (European Conference on Educational Research). “Charting the Way Forward: Education, Research, Potentials and Perspectives“ }}}, year = {{2025}}, } @inproceedings{59252, author = {{Schulze, Jan Roland and Hellmich, Frank}}, publisher = {{University of Belgrade}}, title = {{{Pre-service teachers’ professional self-concept development concerning team-teaching in primary schools. Paper. ECER 2025 (European Conference on Educational Research). “Charting the Way Forward: Education, Research, Potentials and Perspectives“ }}}, year = {{2025}}, } @misc{59921, author = {{Scharlau, Ingrid and Miriam, Körber}}, publisher = {{OSF}}, title = {{{Metaphors in 24 WIRED Level 5 Videos (Data corpus)}}}, doi = {{10.17605/OSF.IO/94A2J}}, year = {{2025}}, } @inproceedings{58713, author = {{Schulze, Jan Roland and Blumberg, Eva and Hellmich, Frank}}, publisher = {{University of Graz}}, title = {{{Effects of a cooperation training on inclusion-related self-concepts of pre-service teachers. Poster Presentation. The 21th Biennial EARLI (European Association for Research on Learning and Instruction) Conference on Learning and Instruction 2025. “Realising Potentials through Education: Shaping the minds and brains for the future” }}}, year = {{2025}}, } @inproceedings{58714, author = {{Finke, Nils and Knickenberg, Margarita and Hellmich, Frank}}, publisher = {{University of Graz}}, title = {{{Feedback as a predictor of primary school students’ motivation and mathematical competence. Single Paper. The 21th Biennial EARLI (European Association for Research on Learning and Instruction) Conference on Learning and Instruction 2025. “Realising Potentials through Education: Shaping the minds and brains for the future” }}}, year = {{2025}}, } @inproceedings{58710, author = {{Knickenberg, Margarita and Hoya, Fabian Karl and Hellmich, Frank}}, publisher = {{University of Graz}}, title = {{{Effects of parental feedback on children’s perceived feedback, motivation and achievement. Single Paper. The 21th Biennial EARLI (European Association for Research on Learning and Instruction) Conference on Learning and Instruction 2025. “Realising Potentials through Education: Shaping the minds and brains for the future” }}}, year = {{2025}}, } @inproceedings{58712, author = {{Görel, Gamze and Franzen, Katja and Hellmich, Frank}}, publisher = {{University of Graz}}, title = {{{Differences in pre-service teachers’ self-efficacy beliefs and willingness for inclusive education. Single Paper. The 21th Biennial EARLI (European Association for Research on Learning and Instruction) Conference on Learning and Instruction 2025. “Realising Potentials through Education: Shaping the minds and brains for the future” }}}, year = {{2025}}, } @inproceedings{58715, author = {{Löper, Marwin Felix and Hellmich, Frank}}, publisher = {{University of Graz}}, title = {{{The role of teacher behaviour in students’ attitudes and support towards peers with disabilities. Single Paper. The 21th Biennial EARLI (European Association for Research on Learning and Instruction) Conference on Learning and Instruction 2025. “Realising Potentials through Education: Shaping the minds and brains for the future” }}}, year = {{2025}}, } @inproceedings{58711, author = {{Finke, Pauline and Schulze, Jan Roland and Hellmich, Frank}}, publisher = {{University of Graz}}, title = {{{Teachers’ self-efficacy beliefs and attitudes concerning inclusive education. Single Paper. The 21th Biennial EARLI (European Association for Research on Learning and Instruction) Conference on Learning and Instruction 2025. “Realising Potentials through Education: Shaping the minds and brains for the future” }}}, year = {{2025}}, } @inproceedings{59244, author = {{Löper, Marwin Felix and Hellmich, Frank}}, publisher = {{University of Belgrade}}, title = {{{ Explanatory factors for students’ attitudes and their supportive behaviour towards peers with social and emotional disabilities. Paper. ECER 2025 (European Conference on Educational Research). “Charting the Way Forward: Education, Research, Potentials and Perspectives“ }}}, year = {{2025}}, } @inproceedings{61309, abstract = {{Service ecosystems reshape service innovation by enabling value co-creation among diverse actors. However, small and medium-sized enterprises and public organizations face significant challenges navigating and leveraging these ecosystems due to resource constraints, knowledge gaps, and partnership difficulties. While digital innovation hubs have been introduced as potential intermediaries to foster innovation, existing models primarily focus on individual solutions and networking rather than orchestrating service innovation. This study investigates the design of a digital service innovation hub as an orchestrating entity that facilitates service innovation within ecosystems. Under the design science research paradigm, we analyze the challenges faced by small and medium-sized enterprises and public organizations and derive design requirements for these hubs. Based on 17 expert interviews and focus group validations, we define the problem space and provide a requirements catalog for designing digital service innovation hubs as a step towards providing holistic support for service innovation initiatives.}}, author = {{Schäfer, Jannika Marie and Liebschner, Jonas and Rajko, Polina and Cohnen, Henrik and Lugmair, Nina and Heinz, Daniel}}, booktitle = {{Proceedings of the 20th International Conference on Wirtschaftsinformatik (WI 2025)}}, keywords = {{service innovation, ecosystem, innovation hubs, SMEs, public sector}}, location = {{Münster, Germany}}, publisher = {{Association for Information Systems (AIS)}}, title = {{{Designing Digital Service Innovation Hubs: An Ecosystem Perspective on the Challenges and Requirements of SMEs and the Public Sector}}}, year = {{2025}}, } @article{55400, abstract = {{This study contributes to the evolving field of robot learning in interaction with humans, examining the impact of diverse input modalities on learning outcomes. It introduces the concept of "meta-modalities" which encapsulate additional forms of feedback beyond the traditional preference and scalar feedback mechanisms. Unlike prior research that focused on individual meta-modalities, this work evaluates their combined effect on learning outcomes. Through a study with human participants, we explore user preferences for these modalities and their impact on robot learning performance. Our findings reveal that while individual modalities are perceived differently, their combination significantly improves learning behavior and usability. This research not only provides valuable insights into the optimization of human-robot interactive task learning but also opens new avenues for enhancing the interactive freedom and scaffolding capabilities provided to users in such settings.}}, author = {{Beierling, Helen and Beierling, Robin and Vollmer, Anna-Lisa}}, journal = {{Frontiers in Robotics and AI}}, keywords = {{human-robot interaction, human-in-the-loop learning, reinforcement learning, interactive robot learning, multi-modal feedback, learning from demonstration, preference-based learning, scaffolding in robot learning}}, publisher = {{Frontiers }}, title = {{{The power of combined modalities in interactive robot learning}}}, volume = {{12}}, year = {{2025}}, } @article{61327, abstract = {{Robot learning from humans has been proposed and researched for several decades as a means to enable robots to learn new skills or adapt existing ones to new situations. Recent advances in artificial intelligence, including learning approaches like reinforcement learning and architectures like transformers and foundation models, combined with access to massive datasets, has created attractive opportunities to apply those data-hungry techniques to this problem. We argue that the focus on massive amounts of pre-collected data, and the resulting learning paradigm, where humans demonstrate and robots learn in isolation, is overshadowing a specialized area of work we term Human-Interactive-Robot-Learning (HIRL). This paradigm, wherein robots and humans interact during the learning process, is at the intersection of multiple fields (artificial intelligence, robotics, human-computer interaction, design and others) and holds unique promise. Using HIRL, robots can achieve greater sample efficiency (as humans can provide task knowledge through interaction), align with human preferences (as humans can guide the robot behavior towards their expectations), and explore more meaningfully and safely (as humans can utilize domain knowledge to guide learning and prevent catastrophic failures). This can result in robotic systems that can more quickly and easily adapt to new tasks in human environments. The objective of this paper is to provide a broad and consistent overview of HIRL research and to guide researchers toward understanding the scope of HIRL, and current open or underexplored challenges related to four themes — namely, human, robot learning, interaction, and broader context. The paper includes concrete use cases to illustrate the interaction between these challenges and inspire further research according to broad recommendations and a call for action for the growing HIRL community}}, author = {{Baraka, Kim and Idrees, Ifrah and Faulkner, Taylor Kessler and Biyik, Erdem and Booth, Serena and Chetouani, Mohamed and Grollman, Daniel H. and Saran, Akanksha and Senft, Emmanuel and Tulli, Silvia and Vollmer, Anna-Lisa and Andriella, Antonio and Beierling, Helen and Horter, Tiffany and Kober, Jens and Sheidlower, Isaac and Taylor, Matthew E. and van Waveren, Sanne and Xiao, Xuesu}}, journal = {{Transactions on Human-Robot Interaction}}, keywords = {{Robot learning, Interactive learning systems, Human-robot interaction, Human-in-the-loop machine learning, Teaching and learning}}, title = {{{Human-Interactive Robot Learning: Definition, Challenges, and Recommendations}}}, year = {{2025}}, }